Experts who push the boundaries of machine learning through cutting-edge research and development. A role that combines deep theoretical knowledge with practical application.
ML Research Engineers are at the forefront of advancing machine learning technologies. They work on developing new algorithms, improving existing models, and ensuring that these innovations can be effectively deployed in real-world applications. These roles are typically found in research-heavy startups, scaleups, and the larger consultancies, where the focus is on pushing the boundaries of what's possible with machine learning.
A typical week is split between theoretical research, coding, and collaboration with cross-functional teams.
Based on advertised midpoints across the 8 UK listings priced in pounds in the last 12 months. Base salary only.
% of 41 listings posted in the last 12 months that mention each skill, extracted from job descriptions.
A typical UK progression for ml research engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.
Assist in research projects, implement basic algorithms, and contribute to team discussions.
Lead specific research initiatives, design and implement complex models, and mentor junior team members.
Oversee multiple research projects, drive innovation, and contribute to strategic decision-making.
Lead the research direction of the organisation, publish influential papers, and guide the development of new technologies.
There's no single route, but most people follow some version of these steps.
Gain a strong foundation in mathematics, computer science, and machine learning through a relevant degree.
Participate in internships or research assistant roles to apply theoretical knowledge in real-world settings.
Focus on a specific area of machine learning, such as deep learning or reinforcement learning, through advanced coursework or projects.
Join a research team in a startup, scaleup, or consultancy to work on cutting-edge projects and collaborate with industry experts.
Take on leadership responsibilities, such as managing research projects and mentoring junior engineers.
Become a recognised expert in the field, publishing influential research and contributing to the broader machine learning community.
ElevenLabs
Join the research team at an AI audio company, training state-of-the-art models across modalities and improving them through post-training, data curation and architectural changes. You'll design rigorous benchmarks and evaluations to judge whether new model iterations are genuinely better, working autonomously in lean, high-velocity teams.
GitHub
Isomorphic Labs · 201–500 people · Series A
Join the model performance and scaling team to implement and optimize LLM post-training methods on frontier models, working with research teams to turn new methods into production systems. The role focuses on distributed training and inference performance, low-precision methods, and frameworks like JAX or PyTorch, in a hybrid London setting.
JAX · PyTorch · NCCL +5
Anthropic · 51–200 people · Series B
Blends research and engineering to advance reinforcement learning for large language models, building agentic capabilities like computer use and autonomous code generation. Involves architecting distributed RL training infrastructure across GPU clusters, designing novel training environments and evaluations, and profiling and optimizing the stack. Works closely with alignment, red team, and production training groups, with a strong pair-programming culture.
PyTorch · TensorFlow · JAX +3
Builds and improves the RL training infrastructure researchers rely on, profiling, debugging and rearchitecting bottlenecks across the stack. Partners with researchers and adjacent engineering teams to ship tooling that speeds up training runs, owning reliability and performance end-to-end.
JAX · PyTorch
ElevenLabs
Owns the data infrastructure behind frontier AI models, building large-scale pipelines for collecting, filtering and transforming training datasets. Also trains classifiers, quality filters and labeling models, and designs curation strategies like deduplication and quality scoring. Fully remote, with tooling work letting researchers explore and train on massive datasets.
Kubernetes
Isomorphic Labs · 201–500 people · Series A
Research Engineers build and optimise state-of-the-art AI models for drug design, working across the full ML lifecycle from experimentation and evaluation to production deployment. The role involves developing robust codebases, data pipelines and training infrastructure, with a focus on architectures such as Transformers, GNNs and diffusion models, in close collaboration with scientists and engineers.
JAX · PyTorch · XLA +6
Luminance · 51–200 people · Series C
Join a small R&D team prototyping and implementing new AI approaches, evaluating output quality, efficiency and feasibility against alternative techniques in a data-driven way. Work spans large dataset handling, modelling and evaluation using Python ML frameworks such as PyTorch, TensorFlow and Scikit-Learn, with senior candidates contributing NLP, computer vision or deep learning expertise.
PyTorch · TensorFlow · Scikit-Learn
Relation Therapeutics
Builds and scales generative and predictive models of cellular behaviour, acting as the bridge between ML theory and implementation. Day to day involves optimising large neural networks, distributed multi-GPU/multi-node training, profiling GPU workloads, and productionising research code for multi-modal biological data.
PyTorch · CUDA · Triton +1
The locations with the most live listings for this role today.
A strong background in computer science, mathematics, or a related field is essential. Advanced degrees, such as a Master's or PhD, are often required for higher-level positions.
Key skills include proficiency in programming languages like Python, a deep understanding of machine learning algorithms, and the ability to conduct rigorous research and experimentation.
The typical progression starts with a junior role, advancing to a senior position, and eventually leading to principal or leadership roles within the organisation.
Common industries include tech, finance, healthcare, and academia. Research-heavy startups and scaleups are also significant employers.
Salaries can vary widely based on experience, location, and industry. For more detailed salary information, please refer to the salary section on this page.
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