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ML Research Engineer Jobs

Experts who push the boundaries of machine learning through cutting-edge research and development. A role that combines deep theoretical knowledge with practical application.

Open roles
24
Salary range
£212k – £633k
Hiring companies
9
View 24 live roles → Get an alert

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.

What the role does

Inside the role of an ML Research Engineer

A typical week is split between theoretical research, coding, and collaboration with cross-functional teams.

  1. 01
    Conduct literature reviews to stay updated on the latest research.
  2. 02
    Design and implement new machine learning algorithms.
  3. 03
    Collaborate with data scientists and engineers to integrate models into products.
  4. 04
    Optimise and test models to improve performance.
  5. 05
    Document findings and contribute to research papers or internal reports.
  6. 06
    Attend meetings to discuss project progress and next steps.
Salary on the board

£212k – £633k

Based on advertised midpoints across the 8 priced listings posted in the last 12 months. Base salary only.

Salary visibility
5% of listings advertise a salary — up from 0% the year before.
By seniority
£k base
Mid
260
636
5 jobs
Skills & tools

What hiring managers ask for

% of 27 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
81%
Machine Learning
52%
PyTorch
44%
Kubernetes
26%
Deep Learning
22%
AWS
19%
CI/CD
19%
Data Pipelines
19%
Docker
19%
Data Engineering
19%
JAX
19%
Reinforcement Learning
19%
Career ladder

From Junior to Principal

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.

  1. Level 1

    Junior ML Research Engineer

    0–2 yrs

    Assist in research projects, implement basic algorithms, and contribute to team discussions.

  2. Level 2

    ML Research Engineer

    2–5 yrs

    Lead specific research initiatives, design and implement complex models, and mentor junior team members.

  3. Level 3

    Senior ML Research Engineer

    5–8 yrs

    Oversee multiple research projects, drive innovation, and contribute to strategic decision-making.

  4. Level 4

    Principal ML Research Engineer

    8+ yrs

    Lead the research direction of the organisation, publish influential papers, and guide the development of new technologies.

Pathway

How to become a ML Research Engineer

There's no single route, but most people follow some version of these steps.

  1. 1

    Academic Foundation

    Gain a strong foundation in mathematics, computer science, and machine learning through a relevant degree.

  2. 2

    Practical Experience

    Participate in internships or research assistant roles to apply theoretical knowledge in real-world settings.

  3. 3

    Specialisation

    Focus on a specific area of machine learning, such as deep learning or reinforcement learning, through advanced coursework or projects.

  4. 4

    Professional Development

    Join a research team in a startup, scaleup, or consultancy to work on cutting-edge projects and collaborate with industry experts.

  5. 5

    Leadership Roles

    Take on leadership responsibilities, such as managing research projects and mentoring junior engineers.

  6. 6

    Thought Leadership

    Become a recognised expert in the field, publishing influential research and contributing to the broader machine learning community.

Live jobs

24 live roles

See all 24 roles →
Isomorphic Labs logo

Research Engineer (LLM Performance), London

This role involves advancing large language models for drug discovery by optimizing post-training methods, scaling frontier AI systems, and solving performance bottlenecks in distributed training and inference. The engineer will collaborate with interdisciplinary teams to translate research into production-ready systems, applying low-precision techniques and performance optimization strategies to real-world drug design challenges. Work focuses on improving model efficiency and scalability within a mission-driven environment combining AI and computational biology.

Isomorphic Labs London, United Kingdom

Research Engineer - Inference

The role involves deploying and optimizing cutting-edge AI models for real-time inference, focusing on performance, reliability, and scalability. You'll work across the stack to improve latency, throughput, and cost efficiency, building high-performance serving systems and tooling that bridge research and production. The position emphasizes autonomous problem-solving and deep optimization in GPU and ML infrastructure environments.

ElevenLabs United Kingdom
Remote Permanent

Research Engineer - Web Crawlers

This role involves building and operating large-scale, distributed web crawlers to source high-quality data for training frontier AI models. The engineer will solve challenges in content extraction, deduplication, and recrawl strategies, while designing pipelines for multilingual and multimedia content. They will also develop tooling for researchers to access and monitor crawled data efficiently.

ElevenLabs United Kingdom
Remote Permanent

Research Engineer, RL Scaling Science

This role involves designing and running large-scale reinforcement learning experiments to understand scaling effects across model size, compute, and task horizon. The engineer will build benchmarks for long-horizon RL, debug system-level issues at the research-infrastructure boundary, and translate validated findings into production training recipes. Work is highly collaborative with research and engineering teams, focusing on empirical science to advance steerable and safe AI systems.

Anthropic London, United Kingdom £375,000 – £640,000 pa
Hybrid Permanent

Research Engineer, Machine Learning (Reinforcement Learning)

This role involves collaborating with researchers and engineers to advance the capabilities and safety of large language models through reinforcement learning. Responsibilities include developing core RL infrastructure, designing novel training environments, and optimizing performance across the stack.

Anthropic London, United Kingdom £260,000 – £630,000 pa
On-site Permanent

Research Data Engineer

Design and build scalable data systems for multi-modal scientific data, enabling efficient analysis and machine learning in a high-performance research environment. Focus on data pipeline architecture, storage optimization, and seamless integration between wet lab and ML teams. Work across cloud-native infrastructure to support large-scale, GPU-intensive model training and rapid iteration.

Relation Therapeutics London, United Kingdom
Hybrid Permanent
Synthesia logo

Staff Research Engineer - Multimodal Generative Modelling

This role involves advancing multimodal generative models that enable natural, real-time interactive conversations combining voice, text, and video. The engineer will lead research in streaming, low-latency systems, design novel architectures using diffusion and neural codecs, and ship end-to-end models into production. Emphasis is placed on emotional expressiveness, conversational realism, and tight integration across audio-visual modalities.

Synthesia London, United Kingdom
Remote Permanent
Synthesia logo

Senior Research Engineer - Data

This role focuses on building and enhancing large-scale data pipelines for AI model training, working with vast video and audio datasets to improve data quality, curation, and annotation. The position bridges applied research and data engineering, collaborating closely with model teams to extract meaningful features and drive performance gains. It emphasizes clean, maintainable code and influence over long-term data infrastructure strategy.

Synthesia London, United Kingdom
Remote Permanent
See all 24 roles →
Top hirers

Companies hiring ml research engineers

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Hiring locations

Where this role is hiring

The locations with the most live listings for this role today.

London

Location
19 open

Cambridge

Location
1 open
FAQs

Common questions

  • 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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