At a Glance
- Tasks: Transform research into impactful AI projects and collaborate with biologists.
- Company: Early-stage company revolutionising bio-analysis with AI automation tools.
- Benefits: Competitive salary, flexible hours, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on innovation and collaboration.
- Why this job: Join a small team at the forefront of AI and life sciences.
- Qualifications: Experience in ML/AI systems and strong Python skills required.
The predicted salary is between 60000 - 80000 £ per year.
We're hiring on behalf of an early-stage company building AI-powered automation tools for automating bio-analysis workflows. Their platform transforms complex, expert-led analytical processes into repeatable, AI-assisted workflows. It's a small team doing serious technical work at the intersection of AI and life sciences.
The Research Engineer owns the research function. You take a direction from product feedback and turn it into results, scoping, experiment design, implementation, data curation, and interpretation. You'll work closely with biologists, scientific contractors, and the broader team, and act as the 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 which metrics matter for agent system performance and which proxies are actually honest
- Build the evaluation infrastructure: benchmarks, harnesses, and the tooling around them
- Develop the research agent stack: memory, in-context learning, test-time compute, and models
- Fine-tune and post-train open models to improve agent performance
- Work with biologists, 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 with evaluations and benchmarks, including tasks where 'correct' is ambiguous
- Familiarity with model training, including the data, optimisation, and evaluation work around it
- Strong engineering fundamentals, fluent in Python and comfortable across the AI/ML stack
- Rigorous approach to experiments: you think about confounds and can defend your results
- Experience with training and evaluation data pipelines, including reproducibility and observability
Nice to have:
- Background in a life science domain, biology, chemistry, medicine, or 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
Research Engineer (D employer: Hlx Life Sciences
Join an innovative early-stage company in London that is at the forefront of AI-powered automation in bio-analysis. As a Research Engineer, you'll be part of a small, dynamic team where your contributions directly impact the development of cutting-edge technology. The company fosters a collaborative work culture, offering ample opportunities for professional growth and the chance to work alongside experts in both AI and life sciences, making it an ideal environment for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer (D
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals 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 potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common interview questions related to AI and bioinformatics, and be ready to discuss your past projects in detail.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications!
We think you need these skills to ace Research Engineer (D
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.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about AI and life sciences. Share specific examples of how your background makes you a great fit for our team and the exciting work we're doing.
Showcase Your Technical Skills:Don’t shy away from detailing your technical expertise in Python and AI/ML. We want to see how you’ve applied these skills in real-world scenarios, so be specific about your contributions and results.
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 this fantastic opportunity to join our innovative team.
How to prepare for a job interview at Hlx Life Sciences
✨Know Your Research Inside Out
Make sure you’re well-versed in the latest trends and breakthroughs in AI and life sciences. Brush up on relevant research papers, especially those related to ML/AI systems and bio-analysis workflows. This will not only show your passion but also help you engage in meaningful discussions during the interview.
✨Prepare for Technical Questions
Expect to dive deep into your technical skills, especially around Python and the AI/ML stack. Be ready to discuss your experience with building LLM-powered systems and how you've approached experiment design and evaluation metrics. Practising coding problems or discussing past projects can really help you shine.
✨Showcase Your Collaboration Skills
Since this role involves working closely with biologists and contractors, be prepared to talk about your teamwork experiences. Share examples of how you’ve successfully collaborated on projects, tackled challenges, and communicated complex ideas to non-technical team members. This will highlight your ability to be a technical sparring partner.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the company’s current projects, their approach to AI in bio-analysis, and how they measure success. This shows your genuine interest in the role and helps you assess if the company is the right fit for you.
