3 hours ago
London, United KingdomSenior
Responsibilities
- Lead technical scoping and architectural decisions for high-impact ML systems and frontier AI model capability testing.
- 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, commercial teams, clients, and frontier labs.
- Act as a technical advisor by translating complex concepts into actionable strategies.
- Mentor junior engineers and help shape engineering culture and technical depth.
Requirements
- Significant experience building and deploying secure, scalable LLM applications, including familiarity with multi-agent harness tooling and AI safety evaluation procedures.
- Deep understanding of the full ML lifecycle and experience operationalizing models built with TensorFlow or PyTorch.
- Strong software engineering expertise and Python skills focused on robust, reusable systems.
- Hands-on experience with AWS, Azure, or GCP, including cloud architecture, infrastructure management, and end-to-end cybersecurity practices.
- Extensive experience with Docker and Kubernetes for building and managing applications at scale.
- Demonstrated ownership, autonomy, and ability to drive projects to completion in fast-paced environments.
- Excellent communication skills with the ability to guide technical teams and senior non-technical stakeholders.
Benefits
- The company 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.
- Applicants may opt out of the AI note-taker used during interviews.
Tech Stack
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