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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.

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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.
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

1 live role

Isomorphic Labs logo

ML Research Engineer, London

Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease.The future is coming. A future enabled and enriched by the incredible power of machine learning. A...

Isomorphic Labs London, United Kingdom
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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