Lead Machine Learning Engineer (Safety)
Lead ML Engineer at Faculty AI (London) driving technical strategy, architecture, and deployment of secure, safety-first AI systems. Day to day: lead capability evaluations and multi-agent tooling with Frontier Labs, advise client leadership, set company-wide ML standards, and mentor engineers using Python, cloud platforms, and container orchestration.
- Compensation
- Not disclosed
- Location
- London
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Part time
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Significant leadership experience designing, deploying, and securing scalable LLM applications, multi-agent frameworks, and AI safety evaluation processes
- Exceptional software engineering fundamentals and advanced Python expertise, building resilient enterprise-ready systems
- Deep hands-on experience with cloud platforms (AWS, Azure, GCP), scalable cloud architectures, container orchestration (Docker, Kubernetes), agent sandboxing, and end-to-end cybersecurity
- Mastery of the full ML lifecycle with proven expertise operationalising production systems using PyTorch, TensorFlow, or similar frameworks
- Thrives in fast-paced, high-growth environments with autonomy, strategic ownership, and a track record of guiding technical decisions
- Communicates persuasively with technical teams and senior non-technical stakeholders, translating complex technical risks into strategic direction
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.
About the role
##
As a Lead Machine Learning Engineer at Faculty, you’ll drive the technical strategy, architecture, and deployment of cutting-edge, secure AI systems across high-stakes domains. Bridging AI research and production impact, you’ll set the benchmark for engineering excellence and operationalise scalable ML infrastructure.
Working alongside Frontier Labs, you will champion practical, safety-first AI frameworks, model capability evaluations, and multi-agent systems. You will act as a principal advisor to client leadership, mentor engineering talent, and steer cross-functional teams to solve critical, real-world AI challenges.
What you'll be doing:
##
- Steering technical strategy, system architecture, and rigorous capability testing for complex ML systems and Frontier AI models
- Architecting production-grade, secure ML software, orchestration tools, and scalable cloud infrastructure
- Establishing company-wide standards and best practices for safe, reliable, and ethical machine learning deployment at scale
- Collaborating with cross-functional teams and executive stakeholders to translate high-impact client needs into resilient AI solutions
- Advising senior clients and partners as a trusted AI safety and technical authority, articulating complex strategies clearly
- Mentoring and developing engineering talent, actively elevating team capabilities and nurturing an inclusive, high-performing culture
- Partnering with Frontier Labs to advance safety evaluation procedures, multi-agent harness tooling, and robust AI governance frameworks
Who we're looking for:
- You have significant leadership experience designing, deploying, and securing scalable LLM applications, multi-agent frameworks, and AI safety evaluation processes.