Senior AI Research Lead

Designing, building, and training novel deep learning models from scratch; debugging low-level framework performance issues; implementing custom layers and loss functions.

Designing and managing complex experiment suites; using advanced features for hyperparameter sweeps, artifact versioning, and collaborative experiment tracking with junior researchers.

Architecting and optimising distributed training jobs; managing compute budgets; leveraging spot instances effectively for cost-efficient large-scale experiments.

Designing and implementing complex data preprocessing pipelines for large datasets; optimising Spark jobs for performance and cost; working with data engineers to define feature stores.

Writing multi-stage Dockerfiles for optimised research images; using Kubernetes (via Kubeflow or managed services) to scale and orchestrate complex research workflows and reproducible environments.

Managing complex Git workflows (e.g., GitFlow); enforcing code quality via CI/CD pipelines (e.g., GitHub Actions) for research codebases; collaborating effectively on shared repositories.

Writing clear, comprehensive research proposals, experiment summaries, and internal documentation; structuring knowledge bases for team collaboration and onboarding.

• Lead AI Research Lead (your direct manager)
• Product Managers (to understand business problems)
• Software Engineering Leads (for model deployment)
• Data Engineering Team (for data access and pipelines)
• Junior AI Researchers (your mentees)

• Academic Researchers (for collaboration and knowledge exchange)
• Technology Vendors (for new tools and platforms)

Pre-trained foundation models (like large language models or vision transformers) are becoming the new baseline. Your value will increasingly come from effectively fine-tuning, adapting, and optimising these massive models for our specific, niche use cases, rather than training everything from scratch.

Parameter-Efficient Fine-Tuning (PEFT) · Domain Adaptation & Transfer Learning · Multimodal Foundation Models · Model Editing & Personalisation

• This week: Pick a small, open-source foundation model (e.g., a small LLM or vision transformer) and experiment with fine-tuning it on a public dataset.
• This month: Apply PEFT techniques to one of your existing models, comparing performance and resource usage to full fine-tuning.
• Month 2: Explore a multimodal dataset and brainstorm how a foundation model could be adapted to extract new insights for our business.
• Month 3: Evaluate different open-source fine-tuning libraries and propose a standard approach for the team.

Quick win: Start experimenting with Hugging Face's PEFT library on a personal project. It's relatively easy to get started and offers immediate insights into model adaptation.

As research problems become more complex, manually searching for optimal architectures, hyperparameters, and data augmentations becomes unsustainable. AutoML tools will increasingly become a core part of the research toolkit, allowing you to explore vast solution spaces more efficiently.

Neural Architecture Search (NAS) · Automated Data Augmentation · Meta-Learning & Few-Shot Learning · Responsible AutoML

• This week: Read a foundational paper on Neural Architecture Search (e.g., NASNet, EfficientNet) to grasp the core concepts.
• This month: Experiment with an open-source AutoML library (e.g., AutoGluon, AutoKeras) on one of your existing datasets to see its capabilities.
• Month 2: Design an experiment to compare your manually designed architecture against an AutoML-generated one, focusing on performance and interpretability.
• Month 3: Propose how AutoML could be integrated into our existing research workflow to accelerate specific parts of our experimentation.

Quick win: Try using an AutoML library for baseline comparisons in your next project. It's a quick way to see if there's an 'easy win' architecture you might have missed.