At a Glance
- Tasks: Transform research ideas into impactful projects and collaborate with scientists on AI-driven bioanalysis.
- Company: Join a pioneering team at Science Machine, revolutionising bioanalysis workflows with AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with a focus on innovation and collaboration in a scientific environment.
- Why this job: Make a real difference in science by developing cutting-edge AI solutions.
- Qualifications: Experience in ML/AI systems and strong Python skills required.
The predicted salary is between 50000 - 70000 € per year.
Science Machine is building agentic AI software for automating bioanalysis workflows. Our platform helps scientists and bioanalysis teams turn complex analytical processes into reliable, repeatable, AI-assisted workflows across data ingestion, analysis, reporting, review, and compliance. The Research Engineer executes our research projects. The work spans agent systems, how we quantify their performance, and fine-tuning open models. Research direction comes from product feedback; you turn it into shipped results. You will work close to the science: formulating problems, designing experiments, shipping implementations, curating data, and interpreting results. You will work with biologists, scientific contractors, and the rest of the team, and act as a technical sparring partner on what to build next.
What you'll do
- Turn research directions into projects: scoping, experiment design, implementation, data, and results.
- Define how we measure system performance: which metrics matter, which proxies are honest, which signals to trust.
- Build the evaluation infrastructure to put those metrics into practice: benchmarks, harnesses, and the tooling around them.
- Develop the research agent stack: memory and in-context learning, test time compute, and models.
- Fine-tune and post-train open models to improve agent performance.
- Work with biologists, scientific contractors, and annotators to build reproducible training and evaluation data pipelines.
- Track what frontier labs are shipping and bring back what's relevant.
Essential experience
- Experience building ML/AI systems in a research-adjacent context (industry, lab, or PhD).
- Experience building LLM-powered systems: prompts, context engineering, agent architectures.
- Experience working with evaluations and benchmarks, including in tasks where 'correct' is ambiguous.
- Familiarity with model training generally, including the data, optimisation, and evaluation work around it.
- Strong engineering fundamentals. Fluent in Python and comfortable across the AI/ML stack.
- Experience running experiments rigorously, in academia or industry. You think about confounds. You can defend your results.
- Experience with training and evaluation data pipelines, including reproducibility and observability.
Nice to have
- Experience in a life-science domain (biology, chemistry, medicine, bioinformatics).
- Post-training experience on LLMs.
- Peer-reviewed publications, or other settings where your ideas were stress-tested.
- Open source contributions to scientific or AI tooling.
Essential qualities
- High ownership: you notice what needs doing and carry it through.
- Skeptical of your own results: you assume a good-looking number is wrong until you understand why it isn't.
- Strong opinions, weakly held: you push back, defend a position, and change your mind when evidence moves.
- Hands-on with the unglamorous parts: data cleaning, contractor coordination, eval annotation, whatever the project needs.
- Reliable under ambiguity: you make progress when problems aren't yet well-defined.
- Curious about science: you want to learn the bioanalysis domain.
- Clear communicator: you can explain technical decisions and results to engineers, scientists, and founders.
Research Engineer in London employer: ScienceMachine
At Science Machine, we pride ourselves on fostering a collaborative and innovative work culture that empowers our Research Engineers to thrive. Located in a vibrant tech hub, we offer competitive benefits, opportunities for professional growth, and the chance to work closely with leading scientists in the bioanalysis field. Join us to contribute to cutting-edge AI solutions while enjoying a supportive environment that values curiosity and ownership.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend relevant meetups or conferences, and connect with potential colleagues 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 skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to ML/AI systems. This gives you a chance to demonstrate your hands-on experience and technical prowess beyond just your CV.
✨Tip Number 3
Prepare for interviews by diving deep into the company’s work and recent projects. Be ready to discuss how your experience aligns with their needs, especially around agentic AI and bioanalysis workflows. Tailor your responses to show you’re the perfect fit!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Science Machine.
We think you need these skills to ace Research Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Research Engineer role. Highlight any relevant projects or research you've done, especially those involving ML/AI systems. We want to see how your background fits into our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about bioanalysis and how your experience can contribute to our team. Be specific about what excites you about the role and how you can help us turn research directions into impactful projects.
Showcase Your Problem-Solving Skills:In your application, don’t just list your skills—show us how you've used them! Share examples of how you've tackled complex problems in past projects, particularly in ambiguous situations. We love candidates who can think critically and adapt as needed.
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy to do—just follow the prompts!
How to prepare for a job interview at ScienceMachine
✨Know Your Research Inside Out
Before the interview, dive deep into the latest trends in AI and bioanalysis. Familiarise yourself with the specific projects the company is working on and be ready to discuss how your experience aligns with their research direction.
✨Prepare for Technical Questions
Expect to tackle questions about ML/AI systems and evaluation metrics. Brush up on your Python skills and be prepared to explain your thought process behind experiment design and data pipelines. Practising coding problems can also help!
✨Show Your Curiosity
Demonstrate your passion for science and bioanalysis during the interview. Ask insightful questions about the company's projects and express your eagerness to learn more about the domain. This shows you’re not just looking for a job, but genuinely interested in contributing.
✨Communicate Clearly
Be ready to explain complex technical concepts in simple terms. The ability to communicate effectively with both engineers and scientists is crucial. Practise articulating your past experiences and results in a way that anyone can understand.
