Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead research in machine learning and develop innovative algorithms for real-world applications.
- Company: Fast-growing AI scale-up focused on sustainable engineering solutions.
- Benefits: Competitive salary, stock options, gym access, and social events.
- Why this job: Join a collaborative team making impactful contributions to the ML community.
- Qualifications: PhD in a technical field and experience in machine learning research.
- Other info: Flexible hybrid work model with excellent career growth opportunities.
The predicted salary is between 36000 - 60000 £ per year.
Cambridge / Hybrid (Flexible 1 day per week in the office)
You MUST have a PhD to apply. Open to recent graduates, through to experienced research leaders (Salary will match your level).
About the Company
We build data-efficient engineering AI software that helps teams make better design and performance optimisation decisions, faster. Our technology reduces simulation and testing requirements without compromising quality — accelerating development and enabling more sustainable engineering. Founded in 2016, the company is a fast-growing, well-funded AI scale-up that has received significant industry recognition.
The Opportunity
We’re looking for a Research Scientist to join a research-focused team working at the cutting edge of artificial intelligence and machine learning. You’ll be part of highly collaborative research groups developing algorithms that are both theoretically rigorous and scalable to real-world complexity. The team’s work directly impacts customer challenges and contributes to the wider ML community — with 75+ papers published and 10 patents filed in the last four years.
What You’ll Be Doing
- Develop and lead your own research programme in probabilistic modelling, Bayesian optimisation, and related fields.
- Collaborate with colleagues to meet research goals and communicate findings internally and externally.
- Contribute to product development and customer research projects, applying cutting-edge ML to real-world problems.
- Take an active role in developing and maintaining open-source libraries such as Trieste, GPflow, and GPflux.
What We’re Looking For
- PhD in a technical field or equivalent experience.
- Published research in machine learning, statistics, or optimisation (conferences and/or journals).
- Experience in decision-making methods (Bayesian optimisation, bandits, reinforcement learning, active learning).
- Background in probabilistic modelling (Gaussian processes, Bayesian neural networks, variational inference, etc.).
- Strong numerical programming skills (Python, NumPy, TensorFlow, PyTorch).
- Interest in applying machine learning to real-world industrial challenges.
- A collaborative mindset, including reviewing code and research and providing constructive feedback.
- A passion for continuous learning and helping others grow.
Google Scholar - Please share your link with your CV/application.
What’s on Offer
- Competitive salary (reviewed annually).
- Pension salary sacrifice scheme.
- Stock options (where applicable).
- Free 24/7 on-site gym.
- Social events, game nights and sports groups.
Apply Now or email me directly chris@socode.co.uk to learn more.
Research Scientist, Machine Learning in Cambridge employer: SoCode Recruitment
Contact Detail:
SoCode Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Scientist, Machine Learning in Cambridge
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with researchers on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your work! Create a portfolio showcasing your research, publications, and any projects you've contributed to. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and being ready to discuss your research in detail. Practice explaining complex concepts in simple terms – it shows you can communicate effectively with both technical and non-technical audiences.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Research Scientist, Machine Learning in Cambridge
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in machine learning and probabilistic modelling. We want to see how your background aligns with the role, so don’t be shy about showcasing your published research and any projects that demonstrate your skills.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI and how you can contribute to our team. We love seeing candidates who are excited about solving complex problems and collaborating with others.
Showcase Your Research Impact: When listing your publications, include links to your Google Scholar profile or any relevant papers. We’re keen to see how your work has contributed to the ML community, so make it easy for us to find and read your research!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at SoCode Recruitment
✨Know Your Research Inside Out
Make sure you can discuss your research in detail, especially any published papers or projects related to machine learning and probabilistic modelling. Be prepared to explain your methodologies and findings clearly, as this will showcase your expertise and passion for the field.
✨Showcase Collaboration Skills
Since the role emphasises teamwork, think of examples where you've successfully collaborated with others on research projects. Highlight how you contributed to group goals and how you handle feedback, as this will demonstrate your collaborative mindset.
✨Prepare for Technical Questions
Brush up on key concepts in Bayesian optimisation, Gaussian processes, and other relevant areas. You might be asked to solve problems or discuss algorithms during the interview, so practice explaining your thought process clearly and confidently.
✨Express Your Passion for Continuous Learning
The company values a growth mindset, so be ready to talk about how you stay updated with the latest developments in machine learning. Share any courses, workshops, or conferences you've attended, and express your enthusiasm for helping others grow in their knowledge too.
