Associate AI Researcher

Role Purpose & Context

Role Summary

The Associate AI Researcher helps out with the nitty-gritty of AI experiments, making sure our senior researchers have what they need to push the boundaries. You'll be running tests, collecting data, and generally supporting the team's bigger projects. This role sits right at the start of our research pipeline, laying the groundwork for what eventually might become a new product or a major improvement to an existing one. When you do this job well, experiments run smoothly, data's clean, and the senior team can focus on the hard thinking. If things go sideways, well, it can slow everyone down and mean missed deadlines for our research goals. The tricky part is learning quickly and not being afraid to ask 'stupid' questions – honestly, there aren't any. The reward? You'll be at the forefront of AI, learning from some seriously smart people, and seeing your contributions directly feed into groundbreaking work.

Reporting Structure

• Reports to: Senior AI Researcher or AI Research Lead
• Direct reports:
• Matrix relationships: Junior AI Scientist, Research Assistant (AI/ML), Graduate AI Engineer,

Key Stakeholders

Internal:

• Senior AI Researchers
• AI Research Leads
• Data Engineers
• MLOps Engineers

External:

• Academic partners (occasionally)
• Open-source communities

Organisational Impact

Scope: You're the engine room for our research. Your meticulous execution of experiments and data handling directly supports the progress of our core AI initiatives. Get it right, and the team moves faster; get it wrong, and we're debugging foundational issues instead of exploring new ideas. Basically, you keep the wheels on the research bus.

Performance Metrics

Quantitative Metrics

1. Metric: Experiment Throughput
2. Desc: Number of documented experiments you've successfully run and logged.
3. Target: >15 documented experiments per quarter
4. Freq: Quarterly review
5. Example: In Q1, you ran 18 experiments, all properly logged in Weights & Biases with clear parameters and results.
6. Metric: Code Reproducibility Rate
7. Desc: Percentage of your experiments that a teammate can re-run from your Git repository and documentation without needing to ask you questions.
8. Target: 100% for assigned tasks
9. Freq: Bi-weekly spot checks and peer reviews
10. Example: A senior researcher picked one of your recent experiments at random, pulled the code, and got the exact same results on their machine, using only your documentation.
11. Metric: Pull Request (PR) Acceptance Rate
12. Desc: The proportion of your code contributions (e.g., to shared libraries, data loaders) that are accepted by the team after review.
13. Target: >80% accepted after minor feedback
14. Freq: Monthly Git history review
15. Example: Out of 10 PRs submitted last month, 9 were merged after addressing small comments, showing you're learning the team's coding standards.
16. Metric: Documentation Completeness
17. Desc: How thoroughly and accurately you document your work, including code comments, experiment logs, and wiki entries.
18. Target: All assigned documentation tasks completed to standard
19. Freq: Weekly review by supervisor
20. Example: Your supervisor noted that the README for your new data loader was clear, covered all dependencies, and included usage examples, making it easy for others to use.

Qualitative Metrics

1. Metric: Proactive Learning & Asking Questions
2. Desc: How often you seek out new knowledge, ask clarifying questions, and show initiative in understanding complex topics, rather than waiting to be told.
3. Evidence: You're asking 'why' something works, not just 'how to run it'. You bring up interesting papers you've read. You're not afraid to admit when you don't understand something and ask for help, but you've clearly tried to figure it out first. You're contributing to team discussions, even if it's just to ask a thoughtful question.
4. Metric: Attention to Detail in Data Handling
5. Desc: Your meticulousness in preparing, cleaning, and verifying data for experiments, catching potential errors before they impact results.
6. Evidence: You spot a mislabelled dataset entry that others missed. You double-check data sources and report inconsistencies. Your data preprocessing scripts are clean and robust, handling edge cases. You're the one who notices the column names don't quite match between two datasets.
7. Metric: Responsiveness to Feedback
8. Desc: How well you take on board feedback from code reviews, experiment critiques, and general guidance, and apply it to future work.
9. Evidence: You don't make the same mistake twice after receiving feedback. You actively seek out feedback on your work. You show a clear improvement in your coding style or experimental design based on previous suggestions. You're not defensive when your work is critiqued, but rather see it as a chance to learn.
10. Metric: Collaboration & Team Support
11. Desc: Your willingness to help teammates, share knowledge, and contribute positively to the team's overall working environment.
12. Evidence: You offer to help a colleague debug their code. You share useful resources you've found. You participate constructively in team meetings. You're generally seen as a helpful and approachable member of the team, even if you're still new.

Primary Traits

• Trait: Insatiable Intellectual Curiosity
• Manifestation: You're the sort who dives deep into a new paper on arXiv just because the abstract sounded interesting, even if it's not directly related to your current project. You'll spend evenings trying to replicate a weird result you saw in a blog post. In meetings, you're always asking 'what if we tried this?' or 'why does it work that way?' – you're genuinely fascinated by how things tick, not just how to run the code.
• Benefit: Honestly, this role isn't about just following instructions; it's about pushing boundaries. Curiosity is the fuel for new ideas. Without it, we'd just be rehashing old solutions, and that's not what research is about. We need people who are driven to explore the unknown, because that's where the real breakthroughs hide.
• Trait: Healthy Skepticism (Especially of Your Own Work)
• Manifestation: When your model gets a surprisingly high accuracy score, your first thought isn't 'brilliant!' but 'what's wrong with it?'. You'll spend more time trying to break your own work, looking for data leaks or subtle biases, than celebrating. You're the one who'll insist on running ablation studies to prove every component of your model is actually doing something useful, not just there by accident. You're quick to point out potential flaws in your own results before anyone else can.
• Benefit: Look, it's easy to fool yourself in AI research. A tiny bug or a weird data split can make a bad model look amazing. If we're not rigorously sceptical, we risk building products on shaky foundations. We need you to be the internal 'quality control' for your own work, ensuring what we build is truly robust and valid, not just a statistical fluke.
• Trait: Gritty Persistence Through Failure
• Manifestation: You've got a GitHub commit history that shows months of methodical iteration on an idea that just isn't quite working yet. When experiment #57 fails to converge after a week of training, you calmly explain what you've learned and what you'll try in experiment #58, rather than getting demoralised. You see each failed experiment as a step closer to the solution, not a dead end. You don't give up when the data's messy or the model's being stubborn.
• Benefit: Truth is, AI research is 99% failure. Most ideas don't work, or they don't work as expected. If you need instant gratification, you'll struggle here. A breakthrough isn't a flash of genius; it's the result of systematically navigating a vast landscape of dead ends. We need people who can keep going when things get tough, who see setbacks as data points, not personal failures.

Supporting Traits

• Trait: Clear Communicator
• Desc: Can explain a complex research idea or a tricky bug in plain English to someone who isn't an AI expert. You'll need to write clear documentation and explain your findings to the team.
• Trait: Organised & Methodical
• Desc: You'll keep your experiments, code, and notes tidy. Messy research is useless research. This means consistent logging, version control, and clear file structures.
• Trait: Collaborative Spirit
• Desc: You're happy to share your code, ask for help, and offer it when you can. Research isn't a solo sport; we learn faster together.
• Trait: Pragmatic Learner
• Desc: You're keen to learn new things, but you also know when to focus on getting the immediate task done. It's about balancing deep dives with practical application.

Primary Motivators

1. Motivator: Solving Hard, Uncharted Problems
2. Daily: You get a buzz from tackling a problem where there isn't an obvious answer. You're excited by the idea of building something that hasn't been done before, even if it's just a small part of a larger research effort.
3. Motivator: Continuous Learning & Skill Mastery
4. Daily: You're always looking for new techniques, frameworks, or theoretical concepts to add to your toolkit. The idea of becoming truly expert in a niche area of AI excites you more than a fancy job title.
5. Motivator: Contributing to Groundbreaking Work
6. Daily: You want your work, even at an entry level, to be part of something bigger – something that could genuinely change how we do things, whether it's a new product or a scientific discovery.

Potential Demotivators

Honestly, if you need immediate, tangible results from every piece of work, you'll probably find this role frustrating. Research is a long game, full of dead ends and incremental progress. You'll spend a lot of time on data cleaning or running experiments that don't quite pan out. If you're looking for a role where every line of code you write goes straight into production next week, this isn't it. Sometimes, your brilliant model might just become a footnote in a paper, or worse, get shelved because the business priorities shifted.

Common Frustrations

1. Spending days trying to reproduce a published paper's results, only to find it's impossible without some 'secret sauce' the authors didn't mention.
2. Realising that 80% of your 'cutting-edge research' time is spent on mundane data cleaning, wrangling mislabelled examples, and writing boilerplate data loaders.
3. Your model works brilliantly on a carefully curated academic dataset, but its performance plummets when exposed to messy, noisy, real-world data.
4. Having to explain to a non-technical person (or even a senior researcher) why your experiment failed, again, and why you need more compute time.
5. Discovering a fundamental bug in your code that invalidates the 'promising' results you were excited about last week.

What Role Doesn't Offer

1. Immediate, direct impact on customer-facing products (most of your work is foundational research).
2. A perfectly clean, curated dataset to work with from day one (expect to get your hands dirty).
3. A clear, linear path where every experiment succeeds and every idea is implemented (failure is a key part of the process).
4. A '9-to-5' mentality if you're truly passionate about the research (sometimes you'll be thinking about a problem long after you've logged off).

ADHD Positives

1. The constant novelty of research problems can be highly engaging for those who thrive on new challenges and intellectual stimulation.
2. The need for rapid iteration and experimentation aligns well with a 'try it and see' approach, rather than getting bogged down in endless planning.
3. Hyperfocus can be a superpower when diving deep into a complex research problem or debugging a tricky model for hours.

ADHD Challenges and Accommodations

1. The large amount of documentation and meticulous logging required for reproducibility might be challenging; we can help with templates and automated tools.
2. Staying organised with multiple experiments running simultaneously can be tough; we use Weights & Biases and structured project templates to keep things clear.
3. Long periods of deep, uninterrupted work are often needed; we can help you set up focus blocks and minimise distractions, and we're flexible about when you do your deep work.

Dyslexia Positives

1. The role often involves visual thinking, pattern recognition, and abstract problem-solving, which are common strengths for dyslexic individuals.
2. Emphasis on conceptual understanding over rote memorisation aligns well with a holistic learning style.
3. Tools that automate code generation and documentation can significantly reduce the burden of writing and proofreading.

Dyslexia Challenges and Accommodations

1. Extensive reading of academic papers and detailed documentation can be demanding; we can provide text-to-speech tools, offer summaries, and encourage verbal explanations.
2. Writing complex code and reports requires precision; we use strong IDEs with linting, grammar checkers, and peer review for all code and documentation.
3. We're happy to discuss alternative ways of presenting your findings, like diagrams, presentations, or verbal summaries, instead of just written reports.

Autism Positives

1. The logical, systematic nature of AI research, with its focus on data, algorithms, and reproducible experiments, can be a great fit.
2. Opportunities for deep, focused work on specific problems, often with clear objectives, can be very appealing.
3. A culture that values objective results and rigorous methodology over social performance can be a comfortable environment.

Autism Challenges and Accommodations

1. Team collaboration and presenting findings, especially to non-technical audiences, might be challenging; we can provide clear guidelines for interactions and support for presentations.
2. Unpredictable changes in research direction or project priorities can be unsettling; we strive for clear communication on changes and provide as much notice as possible.
3. Sensory environment: we can offer quiet workspaces, noise-cancelling headphones, and flexibility for remote work to manage sensory input.

Key Responsibilities

Experience Levels Responsibilities

1. Level: Entry Level (0-2 years)
2. Responsibilities: Under the guidance of a Senior AI Researcher, you'll set up and run experiments using existing model architectures and datasets. This means making sure all the parameters are correct and the data is loaded properly.
3. You'll be responsible for meticulously logging all experiment parameters, results, and observations in Weights & Biases (W&B) or MLflow. Honestly, this is crucial for reproducibility—no shortcuts here!
4. Help out with data cleaning and preprocessing for specific research projects. This often involves writing small Python scripts to transform raw data into something usable, catching those annoying inconsistencies.
5. Write clear, concise documentation for your code and experiments, following our team's templates. Think of it as leaving breadcrumbs for your future self or a teammate.
6. Participate actively in team meetings and research discussions. Don't be shy; ask questions, learn from others, and contribute your thoughts, even if you're just starting out.
7. Keep up-to-date with relevant academic papers and open-source libraries. We expect you to spend some time reading and trying to understand new developments in the field.
8. Support the team with basic infrastructure tasks, like launching pre-configured training jobs on our cloud platforms (AWS SageMaker, GCP Vertex AI) and monitoring their progress.
9. Supervision: You'll have daily check-ins with your assigned Senior AI Researcher or Lead. All your work, especially new code or experiment designs, will be reviewed before it's considered 'done'. We're here to teach you, so expect lots of guidance and feedback.
10. Decision: You won't be making independent strategic decisions. Any technical choices, like which specific library to use for a minor task, should be discussed with your supervisor. If you're unsure, always ask. Escalation is the default for anything beyond routine task execution.
11. Success: You'll be doing well if your experiments are run accurately and on time, your documentation is clear, and you're actively learning and asking thoughtful questions. We want to see you taking on feedback and applying it to improve your work. Basically, we want to see you growing into a proper researcher.

Decision-Making Authority

• Type: Experiment Design & Parameters
• Entry: Propose initial ideas, but execution and final parameters are set by supervisor.
• Mid: Independently design and execute experiments for well-defined problems, with manager review.
• Senior: Design complex experiment suites, define metrics, and make trade-offs with minimal oversight.
• Type: Tool & Library Selection (Minor)
• Entry: Use pre-approved tools and libraries; any new suggestions need supervisor approval.
• Mid: Choose appropriate tools from a pre-approved list for specific tasks; propose new ones with justification.
• Senior: Evaluate and recommend new tools/libraries for adoption across the team, setting standards.
• Type: Data Preprocessing Methods
• Entry: Execute data cleaning scripts provided by others; report anomalies to supervisor.
• Mid: Design and implement data preprocessing pipelines for specific project needs.
• Senior: Architect robust data pipelines and define data quality standards for research.
• Type: Project Timelines & Scope
• Entry: Follow assigned timelines; immediately escalate any potential delays or scope creep to supervisor.
• Mid: Manage your own task timelines within project scope; flag potential issues to manager.
• Senior: Estimate and negotiate project timelines, managing expectations with stakeholders.

Competency Requirements

Foundation Skills (Transferable)

Even as an Associate, you'll need a solid grounding in how to approach problems, communicate your findings, and generally work effectively. These aren't just 'nice-to-haves'; they're essential for thriving in a research environment.

• Category: Communication & Collaboration
• Skills: Clear Written Communication: Can explain technical concepts and experiment results in a way that's easy to understand for peers and supervisors, using tools like Confluence or Notion.
• Active Listening: Genuinely listens to feedback and instructions, asking clarifying questions to ensure understanding.
• Teamwork: Works well with others, shares information, and contributes constructively to group discussions and code reviews.
• Category: Problem Solving & Analysis
• Skills: Logical Reasoning: Can break down a problem into smaller, manageable parts and follow a logical path to a solution.
• Basic Debugging: Able to identify and fix simple errors in code or experiment setups.
• Data Interpretation: Can read charts, graphs, and basic statistical outputs to understand experiment results.
• Category: Learning & Adaptability
• Skills: Curiosity & Initiative: Actively seeks out new knowledge and tries to understand 'why' things work, not just 'how' to do them.
• Feedback Absorption: Takes on board constructive criticism and applies it to improve future work.
• Organisational Skills: Manages tasks, files, and documentation in a structured way to ensure reproducibility.

Functional Skills (Role-Specific Technical)

This is where the rubber meets the road. You'll need a foundational understanding of AI concepts and the tools we use daily. We're not expecting you to invent new algorithms yet, but you should be able to apply existing ones and understand the basics.

Technical Competencies

• Skill: Foundational Deep Learning Concepts
• Desc: You should grasp the basics: what a neural network is, common activation functions, loss functions (e.g., cross-entropy), optimisers (e.g., SGD, Adam), and the idea of backpropagation. You don't need to derive them from scratch, but understand their purpose.
• Level: Intermediate
• Skill: Scientific Method & Basic Experimentation
• Desc: Understand the idea of formulating a hypothesis, designing a simple controlled experiment, and interpreting results. This means knowing what a baseline is and why you need one.
• Level: Basic
• Skill: Data Preprocessing & Feature Engineering
• Desc: Can write scripts to clean, transform, and prepare datasets for model training. This includes handling missing values, normalisation, and basic categorical encoding.
• Level: Intermediate
• Skill: Version Control with Git
• Desc: Proficient with Git for managing your code: committing changes, branching for new features, merging, and resolving basic conflicts. This is non-negotiable for collaborative research.
• Level: Intermediate
• Skill: Basic Algorithm Design & Complexity
• Desc: A basic understanding of common data structures (arrays, lists, dictionaries) and algorithms, and the ability to think about the time and space efficiency (Big O notation) of your code, even if it's just for small scripts.
• Level: Basic

Digital Tools

• Tool: Python (pandas, NumPy, scikit-learn)
• Level: Intermediate
• Usage: Writing data loading scripts, cleaning datasets, performing basic statistical analysis, and implementing baseline ML models.
• Tool: PyTorch or TensorFlow
• Level: Intermediate
• Usage: Building and training known deep learning architectures, debugging model issues, and running experiments designed by senior researchers.
• Tool: Weights & Biases (W&B) or MLflow
• Level: Intermediate
• Usage: Logging experiment parameters, metrics, and artefacts diligently to ensure reproducibility and track progress.
• Tool: Docker
• Level: Intermediate
• Usage: Creating reproducible environments for local development and ensuring your experiments run consistently across different machines.
• Tool: AWS SageMaker / GCP Vertex AI / Azure ML
• Level: Basic
• Usage: Launching and monitoring training jobs on pre-configured cloud instances, understanding basic resource allocation and cost implications.
• Tool: Databricks / PySpark
• Level: Basic
• Usage: Using notebooks to query and process pre-cleaned datasets stored in our data lake, typically for large-scale data preparation.
• Tool: Git (GitHub/GitLab)
• Level: Intermediate
• Usage: Version controlling all your code, collaborating with teammates via pull requests, and managing branches for different experiments.
• Tool: Confluence / Notion
• Level: Intermediate
• Usage: Documenting your research findings, experiment setups, and contributing to shared knowledge bases.

Industry Knowledge

• Area: Core AI/ML Paradigms
• Desc: Understanding the difference between supervised, unsupervised, and reinforcement learning, and knowing when to apply each. You should know what a classification problem is versus a regression problem.
• Area: Ethical AI Principles (Basic)
• Desc: A foundational awareness of biases in data and models, privacy concerns, and the broader societal impact of AI. This isn't just theory; it's about thinking critically about the models we build.
• Area: Research Publication Landscape
• Desc: Knowing about key conferences (e.g., NeurIPS, ICML, ICLR) and pre-print servers (arXiv) where new research is published. You should be able to navigate these to find relevant papers.

Regulatory Compliance Regulations

• Reg: GDPR (General Data Protection Regulation)
• Usage: Understanding the importance of data privacy when handling any personal data in research datasets. Knowing not to use sensitive data without proper anonymisation or consent.
• Reg: Internal Data Governance Policies
• Usage: Adhering to our company's rules for accessing, storing, and processing data, especially regarding sensitive or proprietary information. This means knowing who to ask before using a new dataset.

Essential Prerequisites

• A solid grasp of Python programming, including object-oriented concepts and common data science libraries (pandas, NumPy).
• Foundational knowledge of linear algebra, calculus, and probability/statistics, as applied to machine learning.
• Experience with at least one deep learning framework (PyTorch or TensorFlow), even if it's from academic projects or personal learning.
• Familiarity with Git for version control; you should be comfortable with basic commands like commit, push, pull, and branching.
• A genuine, demonstrable interest in AI research, evidenced by personal projects, academic work, or contributions to open-source.

Career Pathway Context

These prerequisites aren't just checkboxes; they're the foundational tools you'll use every single day. Getting these right means you can hit the ground learning, rather than playing catch-up. They're what we expect you to bring so we can start building on them immediately, moving you towards becoming a fully independent AI Researcher.

Qualifications & Credentials

Emerging Foundation Skills

• Skill: Prompt Engineering & LLM Integration (for Research Support)
• Why: Large Language Models (LLMs) are already transforming how we interact with information and generate text/code. For researchers, this means faster literature reviews, better code generation, and even automated report drafting. Analysts who figure this out will outproduce peers significantly.
• Concepts: [{'concept_name': 'Context Windows & Token Limits', 'description': "Understanding how much information an LLM can 'remember' and process at once, and how to manage it efficiently."}, {'concept_name': 'Temperature & Top-P Sampling', 'description': 'Learning how to control the creativity and randomness of LLM outputs for different tasks (e.g., factual summaries vs. brainstorming ideas).'}, {'concept_name': 'Retrieval Augmented Generation (RAG)', 'description': 'How to connect LLMs to our internal, proprietary research papers and data, so they can answer questions based on our specific knowledge, not just general internet data.'}, {'concept_name': 'Output Validation & Hallucination Detection', 'description': "Crucially, how to critically evaluate LLM outputs and spot when they're making things up or getting facts wrong."}]
• Prepare: This week: Set up GitHub Copilot or a similar AI coding assistant; use it for every piece of code you write.
• This month: Experiment with ChatGPT or Claude to summarise 2-3 academic papers, comparing its output to your own summary.
• Month 2: Try using an LLM to draft the methodology section for one of your simpler experiments, then critically review and refine it.
• Month 3: Explore basic RAG concepts by trying to build a simple Q&A system over a few of our internal research documents.
• QuickWin: Start using Claude or ChatGPT to draft email summaries, brainstorm experiment ideas, or generate code comments today. No approval needed, immediate benefit to your daily workflow.
• Skill: Reproducible MLOps Practices (Early Adoption)
• Why: As research projects mature, they need to be easily handed over to MLOps or product teams. If your experiments aren't reproducible and your models aren't versioned, it creates massive headaches downstream. Learning these habits now will make you invaluable later.
• Concepts: [{'concept_name': 'Data Versioning', 'description': 'Understanding why and how to track changes to datasets, ensuring experiments can always be re-run with the exact data they were trained on.'}, {'concept_name': 'Model Versioning & Registry', 'description': 'How to properly store and track different versions of trained models, along with their associated metadata and performance metrics.'}, {'concept_name': 'Containerisation Best Practices', 'description': 'Moving beyond basic Docker usage to building efficient, multi-stage Dockerfiles for your research environments.'}, {'concept_name': 'Experiment Orchestration (Basic)', 'description': 'Understanding how tools like Kubeflow or Airflow can automate and manage complex sequences of experiments.'}]
• Prepare: This week: Ensure every experiment you run has its data and model versions explicitly logged in W&B or MLflow.
• This month: Refactor one of your existing Dockerfiles to be multi-stage, reducing its size and build time.
• Month 2: Research a simple data versioning tool (e.g., DVC) and try to apply it to a small dataset you're working with.
• Month 3: Shadow an MLOps engineer for a day to see how they productionise models and understand their pain points.
• QuickWin: Make it a habit to always include a `requirements.txt` and a clear `README` in every Git repository. It sounds basic, but it's the first step to reproducibility.

Advancing Technical Skills

• Skill: Deep Learning Architecture Intuition
• Why: As new architectures emerge constantly, simply knowing how to implement them isn't enough. You'll need to develop an intuition for *why* certain architectures work well for specific problems, and how to adapt them, rather than just copying code.
• Concepts: [{'concept_name': 'Attention Mechanisms', 'description': 'Understanding how attention allows models to focus on relevant parts of input data, a core component of Transformers.'}, {'concept_name': 'Convolutional Filters & Receptive Fields', 'description': 'Grasping how CNNs extract features and the impact of different kernel sizes and strides.'}, {'concept_name': 'Recurrent Connections & Memory', 'description': 'Understanding how RNNs (and LSTMs/GRUs) process sequential data and maintain state.'}]
• Prepare: This week: Pick a common architecture (e.g., ResNet or a basic Transformer encoder) and try to implement it from scratch in PyTorch/TensorFlow, without looking at existing libraries.
• This month: Read 2-3 'explainers' on different deep learning architectures (e.g., a GAN, a GNN), focusing on the core ideas rather than just the code.
• Month 2: Participate in a Kaggle competition, specifically trying to adapt an existing architecture to a slightly different problem.
• Month 3: Present a summary of a complex paper's architecture to your team, explaining its intuition and components.
• QuickWin: Whenever you use a new layer or component in your models, take 15 minutes to read its documentation and understand its mathematical basis, not just its API.

Education Requirements

• Level: Minimum
• Req: A Bachelor's degree (or equivalent OFQUAL Level 6 qualification) in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Physics, or a closely related quantitative field.
• Alts: We're pragmatic. If you've got exceptional practical experience, a strong portfolio of personal projects, or significant contributions to open-source AI, we'll absolutely consider that as equivalent. Show us what you can do, not just where you went to uni.
• Level: Preferred
• Req: A Master's degree (or equivalent OFQUAL Level 7 qualification) in one of the fields listed above.
• Alts: While a Master's is a bonus, it's not a deal-breaker. If you've got a Bachelor's and have spent a year or two in a research-heavy role or built some truly impressive projects, that counts for a lot.

Experience Requirements

You'll need 0-2 years of hands-on experience in machine learning or AI, either through academic projects, internships, or entry-level roles. This should include practical experience with deep learning frameworks (PyTorch or TensorFlow), Python programming, and version control (Git). We're looking for demonstrable experience actually building and training models, not just theoretical knowledge. Show us your GitHub, tell us about your final year project, or walk us through that tricky bug you squashed.

Preferred Certifications

• Cert: Deep Learning Specialisation
• Prod: Coursera (DeepLearning.AI)
• Usage: Demonstrates a structured understanding of core deep learning concepts and practical application, often covering PyTorch or TensorFlow.
• Cert: AWS Certified Machine Learning – Specialty
• Prod: Amazon Web Services (AWS)
• Usage: Shows familiarity with cloud-based ML services and MLOps practices, which is helpful for running experiments at scale.

Recommended Activities

• Actively participate in online AI/ML communities (e.g., Kaggle, Hugging Face, Reddit's r/MachineLearning).
• Regularly read and summarise new papers from arXiv to stay current with research trends.
• Contribute to open-source AI projects, even if it's just fixing a small bug or improving documentation.
• Attend virtual conferences, webinars, and workshops on emerging AI topics.
• Build personal projects that explore new algorithms or apply AI to novel datasets.

Career Progression Pathways

Entry Paths to This Role

• Path: University Graduate (BSc/MSc)
• Time: 0-1 year post-graduation
• Path: AI/ML Internship Conversion
• Time: 6-12 months internship experience
• Path: Transition from Data Analyst/Junior Data Scientist
• Time: 1-2 years in a related data role

Career Progression From This Role

• Pathway: AI Researcher (Level 002)
• Time: 2-3 years as an Associate

Long Term Vision Potential Roles

• Title: Senior AI Researcher (Level 003)
• Time: 5-8 years total experience
• Title: AI Research Lead / Staff AI Researcher (Level 004)
• Time: 8-12 years total experience
• Title: Principal AI Researcher (Level 005)
• Time: 12-16 years total experience

How Zavmo Delivers This Role's Development

DISCOVER Phase: Skills Gap Analysis

Zavmo maps your current competencies against all requirements in this job description through conversational assessment. We evaluate your foundation skills (communication, strategic thinking), functional skills (CRM expertise, negotiation), and readiness for career progression.

Output: Personalised skills gap heat map showing strengths and priorities, estimated time to competency, neurodiversity accommodations.

DISCUSS Phase: Personalised Learning Pathway

Based on your DISCOVER results, Zavmo creates a personalised learning plan prioritised by impact: foundation skills first, then functional skills. We adapt to your learning style, pace, and neurodiversity needs (ADHD, dyslexia, autism).

Output: Week-by-week schedule, each module linked to specific job responsibilities, checkpoints and milestones.

DELIVER Phase: Conversational Learning

Learn through conversation, not boring modules. Zavmo uses 10 conversation types (Socratic dialogue, role-play, coaching, case studies) to build competence. Practice difficult QBR presentations, negotiate tough renewals, and handle churn conversations in a safe AI environment before facing real clients.

Example: "For 'Stakeholder Mapping', Zavmo will guide you through analysing a complex enterprise account, identifying key decision-makers, and building an engagement strategy."

DEMONSTRATE Phase: Competency Assessment

Zavmo automatically builds your evidence portfolio as you learn. Every conversation, practice scenario, and application example is captured and mapped to NOS performance criteria. When ready, your portfolio supports OFQUAL qualification claims and demonstrates competence to employers.

Output: Competency matrix, evidence portfolio (downloadable), qualification readiness, career progression score.