At a Glance
- Tasks: Transform research ideas into impactful projects and collaborate with scientists on AI-driven bioanalysis.
- Company: Join a pioneering company focused on agentic AI for scientific workflows.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment 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 (Research) 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 can directly impact the future of AI-assisted workflows in science.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer (Research) 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 put in a good word for you.
✨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 problem-solving abilities, so make it shine!
✨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, we love seeing candidates who take that extra step to engage with us directly.
We think you need these skills to ace Research Engineer (Research) 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 seeing candidates who can think critically and adapt their approach based on feedback, just like we do when developing our AI software.
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language to explain your experiences and achievements. We appreciate a well-structured application that gets straight to the point, as it reflects the clarity we value in our work.
Apply Through Our Website:Don't forget 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 data pipelines, as this will show your hands-on approach.
✨Master the Metrics
Understand the key performance metrics relevant to the role. Be prepared to discuss which metrics matter in evaluating AI systems and how you've implemented them in previous projects. This shows that you can not only build but also critically assess the performance of your work.
✨Showcase Your Collaboration Skills
Since you'll be working closely with biologists and other team members, emphasise your ability to communicate complex ideas clearly. Share examples of how you've acted as a technical partner in past roles, and how you’ve coordinated with diverse teams to achieve project goals.
✨Embrace Curiosity and Ownership
Demonstrate your curiosity about the bioanalysis domain and your proactive nature. Discuss instances where you took ownership of a project or problem, especially when faced with ambiguity. This will resonate well with the essential qualities they’re looking for.
