Lead Machine Learning Engineer
Lead Machine Learning Engineer at Faculty AI (London, on-site) setting technical direction for complex AI/ML projects in the National Security and AI Safety unit — designing scalable production ML systems, leading platform delivery and CI/CD infrastructure with Kubernetes, TensorFlow/PyTorch, and coaching engineers. UK Developed Vetting eligibility may be required.
- Compensation
- Not disclosed
- Location
- London
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Part time
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Expert at defining technical roadmaps and managing project priorities to deliver high-stakes outcomes in high-growth environments
- Mastery of cloud-native ecosystems and orchestration tools like Kubernetes for model lifecycles and CI/CD pipelines
- Proven ability to design large-scale AI-powered platforms and justify critical architectural decisions in high-risk environments
- Expert-level experience operationalising models within frameworks like TensorFlow or PyTorch
- Ability to define engineering standards and architecture patterns for agentic systems to a production-grade bar
- Exceptional ability to align multi-disciplinary technical teams with business objectives and customer needs
- Experience managing and coaching multiple individuals and setting team-wide development goals
- Eligibility for UK Developed Vetting (DV) clearance and willingness to work on site with Government clients from time to time
Full description
Why Faculty?
We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here.
We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.
Our business, and reputation, is growing fast and we’re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology.
AI is an epoch-defining technology, join a company where you’ll be empowered to envision its most powerful applications, and to make them happen.
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About the team
Our National Security and AI Safety business unit is dedicated to advancing the responsible development and deployment of AI in support of national security and global stability. From strengthening mission-critical capabilities across national security and intelligence, to working with frontier labs to provide robust AI safety red teaming and evaluation, we work at the frontier of high-stakes, high-impact missions.
We understand that powerful AI systems bring both transformative opportunities and complex risks and we are proud to partner with Government and the biggest tech organisations in the world to ensure AI is not just transformative but is also secure, trustworthy and safe for all.
Because of the nature of the work we do with our Government clients, you may need to be eligible for UK Developed Vetting (DV) and willing to work on site with our clients from time to time.
About the role
As a Lead Machine Learning Engineer at Faculty, you will set the technical direction for complex AI/ML projects, ensuring models perform at scale and in production over time by balancing technical trade-offs and guiding team priorities.
You will lead the delivery of large-scale AI-powered platforms in high-risk environments while defining project roadmaps across multiple complex workstreams.
This is an ambitious, entrepreneurial leadership role where you will act as a trusted technical expert, defending your architectural rationale to senior stakeholders to ensure we deliver high-quality, high-value outputs.
What you'll be doing:
- Designing, implementing, and maintaining reliable, scalable ML systems while justifying key architectural decisions for production environments.