United Kingdom · Technology and data · Director/VP (16-20 years)

Director of Machine Learning Engineering

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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Experience bandDirector/VP (16-20 years)
Reports toVP of Engineering (ML Platform) or Chief Technical Officer
UK framework levelUsually a director, accountable for a division and its numbers

Also advertised as VP of ML Engineering · Head of ML Platform · Director, AI/ML Infrastructure

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 2 of 5 standards

ATHE Level 7 Extended Diploma in Computing ATHE Ltd · Level 7 · covers 2 of 5 standards

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 3 of 5 standards

OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence OCN London · Level 5 · covers 2 of 5 standards

Pearson BTEC Level 5 Higher National Diploma in Cloud Computing Pearson Education Ltd · Level 5 · covers 2 of 5 standards

ATHE Level 5 Extended Diploma in Computing ATHE Ltd · Level 5 · covers 2 of 5 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Data Science Foundations OTHM Level 7 Diploma in Data Science

Applications of Machine Learning and Artificial Intelligence ATHE Level 7 Extended Diploma in Computing

Machine Learning Pearson BTEC Level 5 Higher National Diploma in Computing for England

Machine Learning Algorithms OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence

Data Analytics and Machine Learning ATHE Level 5 Extended Diploma in Computing

The future skills this job leans on

AI Fluency working with AI as a thinking partner

AI Governance Fluency using AI responsibly

Frontier Fluency the next wave, beyond mainstream AI

Systems Fluency seeing the whole picture

Trust Fluency keeping the digital world safe

Your Plan Illustration

Built for Director of Machine Learning Engineering

5 units that map to this job, from the qualifications that cover it.

Data Science Foundations OTHM Qualifications · covers 2 of 5 standards Level 7
Applications of Machine Learning and Artificial Intelligence ATHE Ltd · covers 2 of 5 standards Level 7
Machine Learning Pearson Education Ltd · covers 3 of 5 standards Level 5
Machine Learning Algorithms OCN London · covers 2 of 5 standards Level 5
Data Analytics and Machine Learning ATHE Ltd · covers 2 of 5 standards Level 5

These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Director of Machine Learning Engineering?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

AI Ethics & Responsible AI Leadership
Quantum Machine Learning (Strategic Awareness)

Why each one is rising

The future skills this job leans on

AI Fluencyworking with AI as a thinking partner
AI Governance Fluencyusing AI responsibly
Frontier Fluencythe next wave, beyond mainstream AI
Systems Fluencyseeing the whole picture
Trust Fluencykeeping the digital world safe

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Director of Machine Learning Engineering plan as you set up, starting with the free Automation Fluency module. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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Keep this read. One email with what AI is changing in this job, the new skills it asks for and where to start free. Nothing else follows unless you confirm your address.

3What this role really is

This role means shaping the machine learning engineering strategy for an organisation, and building the platforms that put it into practice. It involves making sure ML platforms are stable, scalable, and support the wider goals of the business. It carries wide responsibility and real influence over how ML is used.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Cloud Platform (AWS) Strategic

Architecting the entire cloud strategy for ML. Making build vs. buy decisions (e.g., SageMaker vs. Kubeflow). Owning TCO and vendor relationships. Guiding teams on optimal service selection.

Containerisation (Docker & Kubernetes) Architect

Setting enterprise standards for containerisation and orchestration. Driving the strategy for multi-cloud or hybrid-cloud Kubernetes deployments. Ensuring security and operational excellence.

ML Frameworks (TensorFlow & PyTorch) Strategic

Guiding the organisation on which frameworks to standardise for different use cases (e.g., edge vs. cloud). Staying ahead of industry trends and evaluating new framework capabilities for strategic adoption.

Data Orchestration (Apache Airflow) Architect

Designing the overall data orchestration strategy. Evaluating alternatives to Airflow (e.g., Prefect, Dagster). Ensuring data governance and lineage are integrated into the pipeline architecture.

Infrastructure as Code (Terraform) Strategic

Owning the entire IaC framework for the organisation. Enforcing policies and standards through tools like Sentinel. Managing the full lifecycle of infrastructure and ensuring compliance.

Monitoring/Observability (Prometheus & Grafana) Architect

Defining the organisation's observability strategy. Integrating metrics, logging (e.g., ELK stack), and tracing (e.g., Jaeger) into a unified platform to provide comprehensive insights into system health and model performance.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choice	Coming in	Where you are now	The step above
ML Platform Strategy & Roadmap	N/A (Executes tasks within defined strategy)	N/A (Contributes to project segments)	N/A (Leads workstreams within a defined strategy)
Budget Allocation & Spend	No authority (Escalates all spend requests)	No authority (Escalates all spend requests)	Recommends spend up to £5K for tools/resources within project scope; requires Director approval.
Organisational Design & Hiring	No authority (Participates in interviews as requested)	No authority (Participates in interviews as requested)	Provides input on hiring decisions for junior roles; mentors new hires.
Major Architectural Changes	N/A (Implements changes within existing architecture)	Proposes minor architectural improvements within a service; requires Senior Engineer review.	Designs and implements architectural changes for specific workstreams; consults Lead/Staff Engineer.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

ML-Driven Business Value

Direct contribution of ML initiatives to revenue uplift, cost savings, or key operational efficiencies across the business unit.

Target · Exceed £5M in annual incremental revenue or cost savings from ML platforms.

In Q2, the new recommendation engine (built on your platform) drove a 1.5% increase in average order value, contributing £1.2M in additional revenue. The automated fraud detection model reduced false positives by 10%, saving £300K in manual review costs.

ML Platform Adoption & Utilisation

The percentage of new ML models or services deployed using the standardised MLOps platform and the overall usage of key platform features.

Target · Achieve >90% adoption for all new model deployments; maintain >80% active user rate for core platform services.

Out of 15 new models launched this year, 14 used our core MLOps platform (93% adoption). Our feature store saw a 20% increase in daily queries, showing strong internal usage.

Cloud Cost Optimisation for ML

Reduction in cloud infrastructure spend specifically for ML training, inference, and data processing, relative to the scale of operations.

Target · Reduce ML cloud compute and storage costs by >15% year-on-year, while increasing model throughput by 20%.

Implemented a new auto-scaling strategy for inference endpoints, cutting monthly GPU costs by £50K without impacting latency. Migrated a large data pipeline to a more cost-effective Spark configuration, saving £20K per month.

Organisational Health & Talent Retention

Attrition rate within the ML Engineering organisation and key indicators of team engagement and satisfaction.

Target · Maintain an annual voluntary attrition rate below 10% for ML Engineering teams.

Our ML Engineering team's attrition rate was 8% last year, below the industry average. Internal surveys show high satisfaction with career development opportunities and technical challenges.

Strategic Influence & Technical Vision

The extent to which your technical vision for ML is integrated into broader company strategy and your ability to influence executive-level decisions.

Regularly invited to C-suite strategy sessions
your proposals for new ML initiatives are frequently approved
recognised as a thought leader internally and externally
other VPs seek your input on their roadmaps.

Organisational Design & Team Scalability

Your effectiveness in structuring ML engineering teams to scale with business needs, fostering a culture of innovation, collaboration, and high performance.

Successful hiring and onboarding of key leadership roles
clear career progression paths for your teams
positive feedback from direct reports and skip-level reports on mentorship and development
smooth integration of new teams or capabilities.

Risk Management & Resilience

Proactive identification and mitigation of technical, operational, and ethical risks associated with our ML platforms and models.

Few major production incidents (or swift, well-managed recovery)
robust disaster recovery plans in place
proactive engagement with legal/compliance on AI ethics
clear understanding and communication of technical debt and its implications.

External Representation & Brand Building

Your ability to represent the company's ML capabilities externally, attracting top talent and enhancing our industry reputation.

Speaking at major industry conferences
publishing technical articles or whitepapers
active participation in industry forums
positive feedback from recruitment on your involvement in candidate engagement.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy

Driving Large-Scale Transformation

You'll be setting the multi-year roadmap for our ML capabilities, seeing your strategic decisions ripple across the entire organisation and fundamentally change how we operate. This means leading initiatives that span multiple teams and departments, with a clear line of sight to significant business impact.

Leading the initiative to migrate all legacy ML models to a new, unified MLOps platform, resulting in a 30% reduction in deployment time and a 25% decrease in operational costs.

Building and Empowering High-Performing Teams

A significant part of your role is about people. You'll spend time mentoring your direct reports (managers and senior ICs), fostering a culture of technical excellence, psychological safety, and continuous learning. Your satisfaction comes from seeing your teams grow, innovate, and deliver exceptional results.

Successfully hiring two new ML Engineering Managers and seeing their teams achieve ambitious quarterly goals, while also launching a new internal mentorship programme for junior engineers.

Solving Complex, Ambiguous Business Problems

You thrive on tackling challenges that don't have obvious solutions, especially when they involve a mix of technical complexity, organisational dynamics, and business trade-offs. You'll be asked to figure out 'how do we do X with AI?' when X is a nebulous, multi-million-pound question.

Architecting a solution for real-time, personalised customer experiences at a global scale, involving distributed systems, low-latency inference, and a novel approach to data synchronisation across regions.

What frustrates people

The constant tension between short-term business demands and long-term strategic investments in the platform.
Navigating organisational politics and competing priorities from other departments (Product, Data Science, Core Engineering).
The sheer volume of meetings, which can feel like they eat into your strategic thinking time.
Accountability for production incidents that were ultimately caused by upstream dependencies or legacy systems.
Attracting and retaining top-tier ML engineering talent in a highly competitive market.
Getting buy-in for significant architectural changes or technical debt repayment when the immediate business value isn't obvious to non-technical leaders.

What this role does not give you

Daily hands-on coding or deep technical implementation (you'll be guiding, not doing).
A predictable, routine work schedule (expect urgent issues and strategic pivots).
Complete autonomy without executive oversight (you're accountable to the C-suite and board).
An environment free from organisational politics or conflicting stakeholder demands.

8Who you work with

This role directly shapes the technical strategy and operational execution of a business unit's entire ML capability. Your decisions influence multi-million-pound budgets, dictate our speed of innovation, and ultimately determine our competitive position in the market. You're building the future, not just working within it.

Inside the business

C-Suite (CEO, COO, CFO)
VPs and Directors of Product Management
VPs and Directors of Data Science
VPs and Directors of Core Engineering
Legal and Compliance Teams
Finance and Procurement

Outside the business

Strategic Technology Vendors (e.g., AWS, Databricks)
Key System Integrator Partners
Industry Bodies and Standards Organisations
Potential M&A Targets (for technical due diligence)
Recruitment Partners

9What you need before you start

Not a wish list. The things you would be expected to already have.

Extensive experience (16+ years) in software engineering and machine learning engineering, with a significant portion in leadership roles managing large, distributed teams.
Proven track record of defining and executing strategic roadmaps for ML platforms that have delivered measurable business impact.
Demonstrable experience managing multi-million-pound budgets and making high-stakes technical and financial decisions.
Deep architectural expertise in building and operating large-scale, fault-tolerant distributed systems on a major cloud provider (AWS preferred).
Experience presenting complex technical strategies and business cases to C-suite executives and board members.
A strong understanding of MLOps principles and practices, having successfully implemented them at an organisational level.
Experience in hiring, mentoring, and developing senior technical talent and managers.

10What to practise next

Where the job is going, and what to do about it starting this week.

ML Platform as a Product

To truly scale ML, the platform needs to be treated like a product with its own users (data scientists, ML engineers), roadmap, and user experience. This requires a product management mindset applied to infrastructure.

User Journey Mapping for ML Engineers · Feature Prioritisation & Roadmapping · Internal Marketing & Adoption Strategies · Feedback Loops & User Research

This quarter: Read 'Team Topologies' and 'Accelerate' to understand modern platform thinking.
Next 6 months: Partner closely with a Product Manager to learn their methodologies and apply them to your platform roadmap.
Next 12 months: Implement a formal feedback mechanism for your ML platform users and start tracking internal 'customer satisfaction' metrics.
Ongoing: Regularly meet with your platform's key users to understand their challenges and needs.

Quick win: Conduct a 'voice of the customer' survey with your internal data scientists and ML engineers to identify their top 3 pain points with the current platform. Use this to inform your next quarter's priorities.

Advanced Cloud Native Architectures for ML

As ML workloads become more complex and distributed, leveraging advanced cloud-native patterns like service meshes, event-driven architectures, and serverless functions becomes crucial for scale, resilience, and cost-efficiency.

Service Mesh (e.g., Istio, Linkerd) · Event-Driven ML Pipelines · Serverless ML Inference · Multi-Cloud & Hybrid-Cloud Strategies

This quarter: Review our current cloud architecture and identify areas where advanced patterns could yield significant benefits.
Next 6 months: Sponsor a proof-of-concept project for one advanced cloud-native pattern (e.g., a service mesh for inference endpoints).
Next 12 months: Develop a strategic roadmap for adopting more advanced cloud-native patterns across the ML platform.
Ongoing: Engage with cloud provider solution architects and attend their advanced technical workshops.

Quick win: Identify one high-cost or high-latency ML service and challenge your teams to propose a serverless or event-driven alternative, focusing on cost savings and performance improvements.

11Staying current once you are in

What people here do to keep up

Active participation in industry conferences (e.g., KubeCon, AWS re:Invent, NeurIPS, ICML) as an attendee or speaker.
Contributing to open-source ML projects or MLOps frameworks.
Mentoring junior engineers or aspiring leaders within the industry.
Publishing technical articles, whitepapers, or blog posts on ML engineering best practices or strategic insights.
Engaging with academic institutions on cutting-edge ML research or talent pipelines.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

AI Ethics & Responsible AI Leadership

With increasing regulatory scrutiny (like the EU AI Act) and growing public awareness, ensuring our AI systems are fair, transparent, and accountable isn't just a 'nice to have'—it's a business imperative and a significant risk area. As a Director, you'll be accountable for this.

Fairness & Bias Detection
Explainability (XAI)
Privacy-Preserving ML
AI Governance Frameworks

Quantum Machine Learning (Strategic Awareness)

While still nascent, quantum computing has the potential to revolutionise certain ML tasks, particularly in optimisation and complex pattern recognition. As a Director, you need to understand its potential impact, even if it's 5-10 years out, to inform long-term R&D investments and strategic partnerships.

Quantum Supremacy & Qubit Technology
Quantum Algorithms for ML
Hybrid Quantum-Classical Approaches
Quantum-Safe Cryptography

13What you’ll use

What you’ll use

Skills this role draws on

Technical

MLOps (Machine Learning Operations) - Strategic
Distributed Systems Architecture
AI Governance & Ethics
ML Platform Strategy
Cloud Cost Optimisation (ML Focus)

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

1
Principal ML Engineer (from a large tech company)
Often a direct step, as the Principal role focuses on org-wide technical strategy, which is a great foundation for Director-level leadership.
Skills to master
- Transitioning from deep IC technical leadership to managing managers, budget ownership, and broader business unit strategy. Developing executive presence and cross-functional influence.
You're ready to move on when
- Proven track record of defining and driving technical strategy for major ML platforms.
- Experience mentoring and guiding other senior engineers.
- Demonstrable impact on business metrics through technical initiatives.
- Strong communication skills with non-technical stakeholders.
2
ML Engineering Manager (from a larger organisation)
Typically 2-4 years as a successful ML Engineering Manager, demonstrating the ability to manage multiple teams or a large, complex programme.
Skills to master
- Scaling leadership from managing individual teams to managing other managers, owning a larger P&L, and developing a strategic vision for an entire function. Building strong relationships with other Directors/VPs.
You're ready to move on when
- Consistently exceeding team performance goals and delivering on roadmaps.
- Successful hiring, development, and retention of engineering talent.
- Demonstrable ability to manage complex projects with significant cross-functional dependencies.
- Proactive identification and resolution of organisational impediments.
3
Director of Engineering (from a related domain)
Sometimes a direct lateral move, particularly if the previous role involved managing large data or infrastructure teams, with a strong interest and foundational knowledge in ML.
Skills to master
- Deepening expertise in MLOps, ML architecture patterns, and the specific challenges of ML model lifecycle management. Building credibility within the ML community.
You're ready to move on when
- Strong track record in managing large engineering organisations and budgets.
- Proven ability to quickly learn and apply new technical domains.
- Demonstrable interest and foundational understanding of machine learning concepts and applications.
- Excellent strategic and people leadership skills.

15Where this role leads

The long view: This role can lead to a CTO position, or to starting an AI-focused company. It can also lead to board or advisory roles, guiding AI and technology strategy.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Director of Machine Learning Engineering is actually changing.

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Data Science Foundations, worked against Director of Machine Learning Engineering.

Data Science Foundations Level 7

Applied to your work in Director of Machine Learning Engineering

1. To enable the learner to define the scope and landscape of Data Science and differentiate the roles of Data Scientists from other IT professionals. 2. To enable the learner to evaluate key topics within Data Science, including data administration, governance, and big data sources. 3. To enable the learner to describe the architecture and core elements of Apache Hadoop. 4. To enable the learner to analyse the advantages and disadvantages of utilising Artificial Intelligence techniques in a business context. 5. To enable the learner to critically analyse the impact of Big Data on digital transformation within organisations and its effects on users. 6. To enable the learner to review strategies for ensuring data compliance and explain the responsibilities and challenges faced by data specialists.

How the thinking builds

1 Remember
2 Understand
3 Apply
4 Analyse
5 Evaluate
6 Create

An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

See Your Progress Grow Illustration

Director of Machine Learning Engineering

MLOps (Machine Learning Operations) - Strategic
Distributed Systems Architecture
AI Governance & Ethics
ML Platform Strategy
Cloud Cost Optimisation (ML Focus)

This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Director of Machine Learning Engineering is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

VP of Engineering (ML Platform)
3-5 years as a successful Director, demonstrating consistent impact and readiness for enterprise-level leadership.

This is a significant step up, moving from business unit-level strategy to company-wide, enterprise-level vision and execution for all ML/AI initiatives.
- Enterprise AI governance and risk management
- Multi-cloud and hybrid-cloud strategy at a corporate level
- Advanced vendor management and strategic partnerships
- Driving cultural adoption of AI across the entire organisation
Chief AI Officer (CAIO)
5-8 years as a Director or VP, with a clear focus on the strategic application of AI across the entire business and a strong external profile.

This is a C-suite role, responsible for defining and executing the company's overall AI strategy, integrating AI into every aspect of the business, and managing its external perception.
- AI product strategy (across all business units)
- AI research and innovation portfolio management
- Strategic partnerships for AI capabilities
- Ethical AI leadership at the highest level

Working with AI on the job

Working with AI

Where AI is starting to help

As a Director, your time is incredibly valuable. Every hour spent on operational details or sifting through information is an hour not spent on strategic thinking, team development, or business growth. That's where AI comes in.

We're not just talking about automating junior tasks; we're talking about giving you superpowers to amplify your leadership and strategic output. Imagine cutting down on report drafting, instantly getting insights from vast amounts of data, or even having an AI assistant help you prepare for critical board meetings. This isn't science fiction; it's how you'll operate here.

Strategic Planning & Roadmap AI

Use an internal LLM, trained on our company's strategy documents, market research, and past project performance, to generate first drafts of strategic roadmaps, identify potential risks, or suggest innovative initiatives. Ask it to 'propose 3 strategic ML platform investments for the next 18 months, justifying each with potential ROI and risks.' This helps you quickly iterate on ideas and focus on refining, not starting from scratch.

Talent Analytics & Team Optimisation

Feed anonymised performance data, skill matrices, and team feedback into an AI tool to identify skill gaps, predict potential attrition risks, or suggest optimal team compositions for upcoming projects. This helps you make data-driven decisions about talent development and organisational design, rather than relying solely on intuition.

Market Intelligence & Trend Analysis

Integrate AI tools to continuously scan industry reports, competitor announcements, and academic papers to provide you with summarised insights on emerging ML technologies, market shifts, and potential threats or opportunities. Get a weekly digest of 'what you need to know about the latest in MLOps' tailored to our business context.

Board Report & Executive Summary Generation

Consolidate performance metrics, project updates, and financial data into an AI tool to automatically draft comprehensive board reports, executive summaries, or investor updates. You'll spend your time finessing the narrative and strategic implications, not wrestling with formatting and data aggregation. This is about clarity and impact, delivered faster.

Common questions

How do you become a Director of Machine Learning Engineering?

Common routes in include Principal ML Engineer (from a large tech company) (Often a direct step, as the Principal role focuses on org-wide technical strategy, which is a great foundation for Director-level leadership.), ML Engineering Manager (from a larger organisation) (Typically 2-4 years as a successful ML Engineering Manager, demonstrating the ability to manage multiple teams or a large, complex programme.) and Director of Engineering (from a related domain) (Sometimes a direct lateral move, particularly if the previous role involved managing large data or infrastructure teams, with a strong interest and foundational knowledge in ML.). Times vary with prior experience.

Where can a Director of Machine Learning Engineering progress to?

This role can lead on to VP of Engineering (ML Platform) (3-5 years as a successful Director, demonstrating consistent impact and readiness for enterprise-level leadership.) and Chief AI Officer (CAIO) (5-8 years as a Director or VP, with a clear focus on the strategic application of AI across the entire business and a strong external profile.), depending on the skills you build.

What new skills matter most for a Director of Machine Learning Engineering?

Increasingly, AI Ethics & Responsible AI Leadership and Quantum Machine Learning (Strategic Awareness). These are the skills our record of this role lists as growing.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my free Automation Fluency module What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.

Director of Machine Learning Engineering

Where should we send it?

Two short quizzes about you

Will AI change your work as a Director of Machine Learning Engineering?

What's rising in its place

AI Ethics & Responsible AI Leadership

Quantum Machine Learning (Strategic Awareness)

Skills this role draws on

Technical

How you actually get there, here

Principal ML Engineer (from a large tech company)

ML Engineering Manager (from a larger organisation)

Director of Engineering (from a related domain)

The credential is what you can do today. These are what keep you valuable.

Roles this leads to

One to one, on the work you already do

Everything else the record holds

The career path, and where it branches

VP of Engineering (ML Platform)

Chief AI Officer (CAIO)

Where AI is starting to help

Strategic Planning & Roadmap AI

Talent Analytics & Team Optimisation

Market Intelligence & Trend Analysis

Board Report & Executive Summary Generation

Common questions

Want a read on where you stand?