At a Glance
- Tasks: Transform research ideas into impactful projects and collaborate with scientists on AI-driven bioanalysis.
- Company: Join Science Machine, a pioneer in agentic AI for bioanalysis workflows.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Other info: Dynamic team culture with a focus on innovation and scientific curiosity.
- Why this job: Make a real difference in science by automating complex analytical processes with AI.
- Qualifications: Experience in ML/AI systems and strong Python skills are essential.
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.
- 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.
- 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.
- 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 (D in London employer: ScienceMachine
At Science Machine, we pride ourselves on fostering a collaborative and innovative work culture that empowers our Research Engineers to make significant contributions to the field of bioanalysis. Located in a vibrant tech hub, we offer competitive benefits, opportunities for professional growth, and a chance to work closely with leading scientists and industry experts. Join us to be part of a mission-driven team where your ideas are valued, and you can directly impact the future of AI-assisted workflows in science.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer (D 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 to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to discuss your past projects, how you approached challenges, and the results you achieved. Practising common interview questions can also help you feel more confident.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate individuals who want to make an impact in the bioanalysis domain. Your next big opportunity could be just a click away!
We think you need these skills to ace Research Engineer (D 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 excited about the role and how your experience makes you a great fit. Don’t forget to mention your curiosity about science and your hands-on approach to problem-solving.
Showcase Your Projects:If you've worked on any interesting projects, especially in bioanalysis or AI, make sure to include them. We love seeing practical applications of your skills, so share what you've built and the impact it had!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at ScienceMachine
✨Know Your Research Inside Out
Before the interview, dive deep into the specifics of your past research projects. Be ready to discuss how you turned research directions into tangible results, especially in ML/AI systems. Highlight your experience with experiment design and implementation, as this will resonate well with the role.
✨Showcase Your Technical Skills
Make sure to brush up on your Python skills and be prepared to discuss your familiarity with the AI/ML stack. You might be asked about your experience with LLM-powered systems and how you've approached model training and evaluation. Bring examples of your work that demonstrate your engineering fundamentals.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to handle ambiguity and define metrics for system performance. Think of scenarios where you had to measure 'correctness' in ambiguous tasks and how you tackled those challenges. This will show your critical thinking and problem-solving abilities.
✨Communicate Clearly and Confidently
Practice explaining complex technical concepts in simple terms. You'll need to communicate effectively with biologists and other team members, so being able to articulate your thoughts clearly is crucial. Prepare to discuss how you can bridge the gap between technical and non-technical stakeholders.
