[Up to c. $450k Comp Package | Office-Led Working]
Role Overview
We’re partnering with a top-tier, technology-led trading firm where machine learning plays a central role in decision-making and execution. This hire will sit within a highly specialised ML group, contributing to the design and evolution of the firm’s research and production ML ecosystem. This is a hands-on engineering role operating close to research. You’ll help build the systems, tooling, and infrastructure that enable rapid experimentation and efficient deployment of models - while also contributing to how machine learning is applied across a fast-moving, data-rich environment...
Key Responsibilities
- Design and develop infrastructure supporting model training, evaluation, and production deployment
- Improve research workflows to accelerate iteration speed and feedback loops for ML experimentation
- Build clean, scalable tooling and APIs that make ML systems intuitive and efficient to use
- Collaborate with researchers to translate theoretical models into robust, production-ready solutions
- Evaluate and apply a broad range of modelling techniques depending on the problem space
- Maintain high-quality, reproducible research codebases with strong usability and structure
- Contribute to architectural decisions shaping the firm’s ML platform and tooling ecosystem
- Stay close to emerging ML techniques, frameworks, and hardware developments, integrating where valuable
What You’ll Bring…
- 3-8 years’ experience in machine learning engineering, applied ML, or similar technical roles
- Strong academic or practical grounding in mathematics (e.g. optimisation, linear algebra, probability)
- Experience building ML systems end-to-end, from experimentation through to production deployment
- Hands-on experience with modern ML frameworks (e.g. PyTorch, JAX, TensorFlow or similar)
- Proven ability to structure and maintain clean, reliable, and reproducible research code
- Solid software engineering fundamentals, including system design and API development
- Genuine interest in state-of-the-art ML research and willingness to explore new techniques
- Ability to critically assess modelling approaches and choose appropriate methods for different problems
- Strong communication skills and ability to collaborate closely with researchers and engineers
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