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 collaboration.
- Why this job: Make a real difference in science by automating complex analytical processes with cutting-edge AI.
- Qualifications: Experience in ML/AI systems, strong Python skills, and a passion for scientific inquiry.
The predicted salary is between 60000 - 80000 € 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.
Experienced Research Engineer in London employer: ScienceMachine
At Science Machine, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to take ownership of their projects and drive meaningful change in the bioanalysis field. Located in a vibrant tech hub, we offer competitive benefits, opportunities for professional growth, and the chance to work alongside leading scientists and engineers in a dynamic environment where your contributions directly impact the future of AI-assisted workflows.
StudySmarter Expert Advice🤫
We think this is how you could land Experienced 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 professionals on LinkedIn. We can’t stress enough how valuable personal connections can be in landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML/AI systems. This is your chance to demonstrate your hands-on experience and technical prowess, so make it shine!
✨Tip Number 3
Prepare for interviews by diving deep into the company’s work and recent projects. We recommend you come armed with questions and insights about their research direction. This shows you’re genuinely interested and ready to contribute.
✨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, we love seeing candidates who take the initiative to engage directly with us.
We think you need these skills to ace Experienced Research Engineer in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Research Engineer role. Highlight your experience with ML/AI systems and any relevant projects you've worked on. We want to see how your skills align with our mission at Science Machine!
Showcase Your Problem-Solving Skills:In your application, give examples of how you've tackled complex problems in research or engineering. We love candidates who can demonstrate their ability to turn research directions into actionable projects, so don't hold back!
Be Clear and Concise:When writing your application, keep it straightforward and to the point. Use clear language to explain your technical decisions and results. Remember, we value strong communication skills, so make sure your application reflects that!
Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way for us to receive your details 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 show your hands-on approach.
✨Metrics Matter
Understand the importance of performance metrics in your work. Be prepared to discuss which metrics you’ve used in previous projects and why they were chosen. This will demonstrate your analytical thinking and ability to quantify system performance effectively.
✨Showcase Your Collaboration Skills
Since you'll be working closely with biologists and other team members, share examples of how you've successfully collaborated in the past. Talk about how you acted as a technical sparring partner and how you communicated complex ideas clearly to non-technical stakeholders.
✨Embrace Ambiguity
Be ready to discuss how you handle uncertainty in research. Share experiences where you made progress despite unclear problems, showcasing your reliability under ambiguity. This quality is essential for the role, so make sure to highlight it!
