At a Glance
- Tasks: Build scalable ML infrastructure and translate research into production-quality systems.
- Company: Exciting AI-native startup reinventing material discovery with cutting-edge technology.
- Benefits: Competitive salary, innovative projects, and the chance to work with world-class researchers.
- Other info: Dynamic environment with significant technical ownership and career growth opportunities.
- Why this job: Join a well-funded company and accelerate global scientific discovery from day one.
- Qualifications: Strong ML engineering experience and excellent Python skills with modern architectures.
The predicted salary is between 72000 - 88000 £ per year.
We're partnering with one of Europe's most exciting AI-native startups that's building an autonomous AI platform to fundamentally reinvent how new materials are discovered. Rather than relying on simulations alone, they're combining cutting-edge machine learning with a high-throughput experimental laboratory, creating a closed-loop system where AI designs new materials, experiments validate them, and real-world results continuously improve the models. Backed by $60M from leading global investors, they've assembled an exceptional team spanning AI, physics, materials science and engineering, and are now looking for outstanding Machine Learning Research Engineers to help build the infrastructure powering the next generation of AI for Science.
You'll be working on:
- Building scalable ML infrastructure for frontier AI research
- Translating novel research into production-quality ML systems
- Distributed training and inference across large GPU clusters
- Optimising model performance, training pipelines and experimentation workflows
- Developing multimodal data pipelines spanning simulations, laboratory data and scientific literature
- Working alongside world-class AI researchers, engineers and scientists to deploy models into a real-world autonomous experimentation platform
We're looking for:
- Strong Machine Learning Engineering experience
- Deep knowledge of modern ML architectures (Transformers, GNNs, Diffusion Models, etc.)
- Excellent Python skills with PyTorch and/or JAX
- Experience building scalable production ML systems
- Someone who enjoys turning cutting-edge research into robust, high-performance software
Bonus experience:
- Distributed GPU training (multi-GPU / multi-node)
- Scientific machine learning or simulation environments
- Performance optimisation and systems engineering
- Docker, Kubernetes, GCP or similar infrastructure
- Scientific computing or research engineering backgrounds
Why this opportunity? This is a chance to join an exceptionally well-funded AI-for-Science company at an early stage and help build technology capable of accelerating scientific discovery in areas that matter globally. You'll work alongside some of the world's leading researchers on genuinely novel problems, with significant technical ownership from day one.
Research Engineer, Machine Learning in London employer: Generative
As a Machine Learning Researcher at our London-based AI lab, you'll be part of a dynamic and ambitious team dedicated to solving significant scientific challenges through innovative AI solutions. We foster a collaborative and inclusive work culture that values curiosity and low ego, offering flexible hybrid working arrangements and opportunities for professional growth in a supportive environment. Join us to make a tangible impact from day one, as you contribute to cutting-edge research that accelerates the discovery of commercially valuable materials.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer, Machine Learning in London
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Generative.
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We think you need these skills to ace Research Engineer, Machine Learning in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Generative, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Generative. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Generative
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Generative!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
