10 months ago
London, United KingdomSenior
Responsibilities
- Lead technical scoping and architectural decisions for high-impact machine learning systems.
- Design and build production-grade ML software, tools, and scalable infrastructure.
- Define and implement standards and best practices for deploying machine learning at scale.
- Collaborate with engineers, data scientists, product managers, and commercial teams on client challenges and opportunities.
- Advise customers and partners by translating complex technical concepts into actionable strategies.
- Mentor junior engineers and help shape the team’s engineering culture and technical depth.
Requirements
- Significant experience across the full machine learning lifecycle, including operationalising models built with TensorFlow or PyTorch.
- Deep software engineering expertise and strong Python skills focused on robust, reusable systems.
- Hands-on experience architecting cloud infrastructure with AWS, Azure, or GCP, including security considerations.
- Extensive experience with Docker and Kubernetes for building and managing applications at scale.
- Ability to demonstrate ownership and autonomy in fast-paced, high-growth environments.
- Strong communication skills and confidence working with technical teams and senior non-technical stakeholders.
Benefits
- Faculty is open to conversations about part-time hours.
- The interview process includes a talent team screen, pair programming interview, system design interview, and commercial interview.
- Candidates may opt out of the AI note-taker used during interviews by notifying the company.
Tech Stack
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