At a Glance
- Tasks: Transform research ideas into impactful projects and collaborate with scientists on innovative AI solutions.
- Company: Join Science Machine, a pioneering company 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 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 employer: ScienceMachine
At Science Machine, we pride ourselves on fostering a collaborative and innovative work environment where Research Engineers can thrive. Our commitment to employee growth is evident through hands-on projects that directly impact the scientific community, alongside opportunities for continuous learning in the rapidly evolving field of AI and bioanalysis. Located in a vibrant tech hub, we offer competitive benefits and a culture that values curiosity and ownership, making us an exceptional employer for those seeking meaningful and rewarding careers.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend relevant meetups, and connect with professionals on LinkedIn. We can’t stress enough how personal connections can lead to job opportunities.
✨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 potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios related to research engineering. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨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 are proactive about their job search!
We think you need these skills to ace Research Engineer
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 think critically and adapt their approach based on feedback, so don't hold back on sharing your thought process!
Be Clear and Concise:When writing your application, keep it straightforward and to the point. Use clear language to explain your experiences and achievements. We appreciate clarity, especially when it comes to technical details, so make sure we can easily understand your contributions.
Apply Through Our Website:We encourage you to submit your application directly through our website. This way, you’ll ensure it reaches us without any hiccups. 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 latest trends in AI and bioanalysis. Familiarise yourself with the specific projects and technologies that the company is working on. This will not only help you answer questions confidently but also show your genuine interest in their work.
✨Prepare for Technical Questions
Expect to be quizzed on your experience with ML/AI systems and how you've tackled ambiguous problems in the past. Brush up on your Python skills and be ready to discuss your approach to experiment design and evaluation metrics. Real-world examples will make your answers stand out!
✨Showcase Your Collaboration Skills
Since the role involves working closely with biologists and other team members, be prepared to discuss how you've successfully collaborated in previous projects. Highlight instances where you acted as a technical partner and how you navigated challenges together.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the company's research direction, team dynamics, and how they measure success. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.
