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Faculty (faculty.ai)

Senior Machine Learning Engineer

Posted 8 Days Ago
Be an Early Applicant
In-Office
London, Greater London, England
Senior level
In-Office
London, Greater London, England
Senior level
As a Senior Machine Learning Engineer, you will lead the development of AI systems, design production-grade ML software, and mentor junior engineers while engaging with clients on technical strategies.
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About Faculty


At Faculty, we transform organisational performance through safe, impactful and human-centric AI.

With more than a decade of experience, we provide over 350 global customers with software, bespoke AI consultancy, and Fellows from our award winning Fellowship programme.

Our expert team brings together leaders from across government, academia and global tech giants to solve the biggest challenges in applied AI.

Should you join us, you’ll have the chance to work with, and learn from, some of the brilliant minds who are bringing Frontier AI to the frontlines of the world.

Bringing medicine to patients is complex, expensive and high-risk. Faculty’s Life Science’s team is concentrated on building AI solutions which optimise the research and commercialisation of life-changing therapies.

We partner with major pharma firms, academic research centres and MedTech start-ups to design and deliver solutions which address critical healthcare challenges, and help to democratize health for all.

#LI-PRIO

About the role:

As a Senior Machine Learning Engineer, we’ll look to you to lead development and deployment of cutting-edge AI systems for our diverse clients. You’ll design, build, and deploy scalable, production-grade ML software and infrastructure that meets rigorous operational and ethical standards.

This is an ambitious, cross-functional role requiring a blend of technical expertise, engineering leadership, and confident client-facing skills.

What you'll be doing:

  • Leading technical scoping and architectural decisions for high-impact ML systems

  • Designing and building production-grade ML software, tools, and scalable infrastructure

  • Defining and implementing best practices and standards for deploying machine learning at scale across the business

  • Collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities

  • Acting as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies

  • Mentoring and developing junior engineers, actively shaping our team's engineering culture and technical depth

Who we're looking for:
  • You understand the full ML lifecycle and have significant experience operationalising models built with frameworks like TensorFlow or PyTorch

  • You bring deep expertise in software engineering and strong Python skills, focusing on building robust, reusable systems

  • You have demonstrable hands-on experience with cloud platforms (e.g., AWS, Azure, GCP), including architecture, security, and infrastructure

  • You've extensive experience working with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale

  • You thrive in fast-paced, high-growth environments, demonstrating ownership and autonomy in driving projects to completion

  • You communicate exceptionally well, confidently guiding both technical teams and senior, non-technical stakeholders

What we can offer you:

The Faculty team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day.

Faculty is the professional challenge of a lifetime. You’ll be surrounded by an impressive group of brilliant minds working to achieve our collective goals.
Our consultants, product developers, business development specialists, operations professionals and more all bring something unique to Faculty, and you’ll learn something new from everyone you meet.

Top Skills

AWS
Azure
Docker
GCP
Kubernetes
Python
PyTorch
TensorFlow

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