Machine Learning Research Engineer

Role Overview

We’re representing a globally respected quantitative investment firm that combines advanced research with cutting-edge engineering to solve complex problems across financial markets. As part of its continued expansion in machine learning, the firm is investing in talent that can push both modelling capability and computational performance forward. They are looking for a Machine Learning Research Engineer to help design and implement next-generation ML systems used in large-scale data analysis and decision-making. This role blends research thinking with deep technical execution - focusing on how models are built, optimised, and run efficiently in demanding production environments. You’ll work closely with researchers and engineers to turn novel ideas into scalable systems that deliver real impact...

Key Responsibilities

• Explore and implement advanced machine learning approaches, bringing new concepts into working systems
• Shape how ML workloads are executed, including training efficiency, scaling, and runtime performance
• Address complex system-level challenges across compute, memory, and data flow
• Contribute to deploying models into live environments where outputs directly influence business outcomes
• Build early-stage prototypes to test ideas and validate technical feasibility
• Collaborate with research teams to improve experimentation speed and iteration cycles
• Enhance the performance and stability of ML pipelines operating at scale
• Identify system constraints early and help guide longer-term architectural decisions

What You’ll Bring…

• 4-7+ years’ experience across machine learning, applied research, or performance-focused ML engineering (flexible for exceptional early-career or PhD candidates)
• Strong grounding in modern machine learning techniques and their practical application
• Experience taking ideas from concept through to efficient, working implementations
• Deep interest in performance, including optimisation of compute-heavy workloads
• Hands-on experience with ML frameworks such as PyTorch, JAX, or similar ecosystems
• Familiarity with GPU-based workloads or acceleration techniques
• Exposure to high-performance or large-scale compute environments
• Strong programming ability, with a focus on building robust and efficient systems
• Ability to work through complex technical challenges with a pragmatic, solution-oriented approach
• (Preferred) Advanced academic background in a technical discipline such as Computer Science, Mathematics, Physics, or related field