4 hours ago
London, United KingdomSenior
Responsibilities
- Build and deploy production-grade machine learning software, tools, and infrastructure.
- Create reusable and scalable solutions for delivering ML systems and testing frontier AI model capabilities.
- Collaborate with engineers, data scientists, commercial leads, clients, and partners to solve critical challenges.
- Lead technical scoping and architectural decisions to ensure project feasibility and impact.
- Define and implement standards for deploying machine learning at scale.
- Advise customers and partners by translating complex ML concepts for technical and non-technical stakeholders.
Requirements
- Experience building LLM applications and familiarity with multi-agent harness tooling and AI safety evaluation procedures.
- Strong Python skills and solid software engineering best practices.
- Hands-on experience with cloud platforms and infrastructure such as AWS, Azure, or GCP, including architecture and security.
- Experience with Docker and Kubernetes for building and managing applications at scale.
- Understanding of core machine learning concepts, including probability, statistics, and common learning techniques.
- Experience across the full machine learning lifecycle and operationalising models built with Scikit-learn, TensorFlow, or PyTorch.
- Strong communication skills and the ability to guide technical teams and advise non-technical stakeholders.
- Ability to work autonomously, own scope, and deliver solutions in a fast-paced environment.
Benefits
- Part-time hours are open for discussion.
- 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.
- The company supports applications from people of all backgrounds and encourages candidates who meet some but not all requirements to apply.
Tech Stack
Categories
