At a Glance
- Tasks: Transform research into impactful AI projects and collaborate with biologists and scientists.
- Company: Early-stage company revolutionising bio-analysis with AI-powered automation tools.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Exciting opportunity to work on innovative projects with potential for significant career advancement.
- Why this job: Join a small team at the forefront of AI and life sciences, making a real difference.
- Qualifications: Experience in ML/AI systems, strong Python skills, and a rigorous experimental approach.
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
Experienced Research Engineer employer: Hlx Life Sciences
Join an innovative early-stage company at the forefront of AI and life sciences, where your contributions as a Research Engineer will directly impact the development of cutting-edge automation tools. With a collaborative work culture that values technical expertise and creativity, you'll have ample opportunities for professional growth while working alongside passionate biologists and scientists in the vibrant city of London. Enjoy the unique advantage of being part of a small, dynamic team that is making significant strides in transforming complex analytical processes into efficient, AI-assisted workflows.
StudySmarter Expert Advice🤫
We think this is how you could land Experienced Research Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and research work. This is your chance to demonstrate your expertise in ML/AI systems and make a lasting impression on potential employers.
✨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 process.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented individuals like you. Plus, applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Experienced Research Engineer
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 your experience in ML/AI systems and any relevant projects you've worked on, especially those involving bio-analysis or life sciences.
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 you've turned research directions into successful projects, and don't forget to mention your collaborative spirit!
Showcase Your Technical Skills:We want to see your engineering fundamentals shine! Be sure to include your proficiency in Python and any experience with model training and evaluation. If you've worked on LLM-powered systems, make that stand out!
Apply Through Our Website:For the best chance of getting noticed, apply directly through our website. It helps us keep track of applications and ensures you’re considered for the role. Plus, we love seeing candidates who take that extra step!
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 your past projects and be ready to discuss how they relate to the role. This shows you’re not just a candidate, but someone genuinely passionate about the field.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions about ML/AI systems and experiment design. Be prepared to explain your thought process behind scoping projects and how you handle ambiguous evaluations. Practising common interview questions can help you articulate your experience clearly.
✨Showcase Collaboration Skills
Since this role involves working closely with biologists and contractors, highlight your teamwork experiences. Share examples of how you’ve successfully collaborated on projects, especially in cross-disciplinary teams. This will demonstrate your ability to be a technical sparring partner.
✨Ask Insightful Questions
Prepare thoughtful questions that show your interest in the company’s mission and the specifics of the role. Inquire about their current projects or challenges they face in building AI-powered tools. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.
