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- Tasks: Build large-scale scientific and AI workflows that drive groundbreaking research.
- Company: Basecamp Research, a leader in exploring unseen biodiversity for biotech solutions.
- Benefits: Competitive pay, medical cover, flexible work, and generous PTO.
- Why this job: Join a dynamic team and make a real impact in biotechnology and AI.
- Qualifications: 1–5 years in Software, Data, or ML Engineering; Python and Go skills required.
- Other info: Collaborative culture with opportunities for personal and professional growth.
The predicted salary is between 36000 - 60000 £ per year.
At Basecamp Research, we explore the world’s unseen biodiversity to unlock nature-designed solutions for biotechnology’s hardest challenges. Through expeditions, partnerships, and large-scale sequencing efforts, we’ve built one of the world’s most unique metagenomic datasets - powering next-generation protein and genomics foundation models. Our work sits at the intersection of machine learning, synthetic biology, large-scale data, and exploratory science, with applications in therapeutics, materials, industrial enzymes, and beyond. We’re an inclusive, interdisciplinary environment where biologists, ML researchers, software engineers, and field scientists work side‑by‑side to push the boundaries of what’s possible.
We’re looking for a Software Engineer who is excited about building large-scale scientific and AI workflows. You’ll report directly to the Software Engineering Lead and contribute to systems that sit at the heart of Basecamp’s research and discovery engine. Software Engineering at Basecamp designs and operates the systems that accelerate the evaluation of biological and AI models, support high-throughput scientific analysis, integrate computation into scientific decision-making, and unlock capabilities that wouldn’t exist without strong engineering. Joining the team means helping build the internal infrastructure and tooling that drive how biology is analysed, designed, and explored across the company. From day one, you’ll write production code, support high-throughput pipelines, and help shape core internal tools as they evolve. This role offers significant ownership and the opportunity to grow into specialised areas as the team expands.
You will:
- Develop and maintain data-processing, inference, and analysis workflows used daily by scientists and ML researchers
- Contribute to large-scale, containerised pipelines deployed across HPC and Kubernetes environments
- Build internal tools (APIs, CLIs, dashboards) that support biological and machine-learning workflows
- Extend and automate orchestration using Dagster or Temporal to improve reproducibility and observability
- Work on performance, logging, monitoring, and operational reliability across distributed systems
- Collaborate with Platform Engineering on infrastructure usage, GPU scheduling, and cluster reliability
- Partner with scientists to understand biological workflows and help translate them into scalable, automated systems
- Participate in technical design discussions and code reviews, contributing ideas and improvements to the team’s engineering practices
This role gives you autonomy early, and space to help define key parts of our technical stack.
About you
You don’t need a biology background, just curiosity, strong fundamentals, and eagerness to learn. You bring:
- 1–5 years of experience (or equivalent projects) in Software, Data, ML, or Infrastructure Engineering
- Proficiency in Python and Go
- Experience with Docker, Kubernetes, and cloud-native development
- Familiarity with workflow systems (e.g., Dagster, Temporal, Airflow)
- Comfort with Linux systems and shell scripting
- Interest in large-scale data, ML workflows, or scientific computing
- A pragmatic, builder mindset – you enjoy improving processes and automating complex tasks
- Curiosity, collaboration, and readiness to work across disciplines
Nice to have:
- Exposure to AWS or Azure
- Experience with observability tools (Prometheus, Grafana, Datadog)
- Familiarity with ML training or inference systems
- Any exposure to bioinformatics, genomics, or biological data tools
If you're excited about the space but don’t tick all the boxes, we still encourage you to apply.
What we can offer in return:
- The opportunity to be a key member in an exciting, dynamic, and fast-moving field.
- A fun, flexible, and supportive work environment in the centre of London, and an emphasis on collaboration and personal development.
- Highly collaborative culture, we always work cross team and cross function, with engineers, data scientists, and biologists working directly together to tackle complex issues.
- Competitive compensation including equity
- Comprehensive medical cover with AXA Critical Illness and Group Income Protection
- Pension Scheme
- Generous PTO
- Enhanced parental policy
- Bike2Work Scheme
Do you want to join our team as our new Software Engineer? Then we’d love to hear about you!
Software Engineer employer: Basecamp Research
Contact Detail:
Basecamp Research Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Engineer
✨Tip Number 1
Network like a pro! Reach out to current employees at Basecamp Research on LinkedIn or other platforms. A friendly chat can give you insider info and might just get your application noticed.
✨Tip Number 2
Show off your skills! If you have a GitHub or portfolio, make sure it’s up to date with your best projects. Highlight any relevant work that aligns with the role of Software Engineer, especially in large-scale data or ML workflows.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and Go skills. Practice coding challenges and be ready to discuss your thought process. Remember, they want to see how you approach problem-solving!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets into the right hands. Plus, it shows you’re genuinely interested in joining the Basecamp team.
We think you need these skills to ace Software Engineer
Some tips for your application 🫡
Show Your Passion: When writing your application, let your enthusiasm for software engineering and the intersection of biology and AI shine through. We want to see your excitement about building large-scale scientific workflows and how you can contribute to our mission.
Tailor Your CV: Make sure your CV highlights relevant experience, especially in Python, Go, and any work with Docker or Kubernetes. We love seeing how your skills align with what we do at Basecamp Research, so don’t hold back on showcasing your projects!
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so make sure to articulate your experiences and skills without unnecessary fluff. This helps us quickly understand how you fit into our team.
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 the role. Plus, it shows you’re keen to join our team!
How to prepare for a job interview at Basecamp Research
✨Know Your Tech Stack
Make sure you’re familiar with the technologies mentioned in the job description, like Python, Go, Docker, and Kubernetes. Brush up on your knowledge of workflow systems like Dagster or Temporal, as these will likely come up during technical discussions.
✨Show Your Curiosity
Basecamp Research values curiosity, so be prepared to discuss how you've approached learning new technologies or solving complex problems in the past. Share examples that highlight your eagerness to explore and understand different disciplines, especially in relation to software engineering and scientific workflows.
✨Prepare for Collaboration Questions
Since the role involves working closely with scientists and other engineers, think about times when you’ve successfully collaborated across teams. Be ready to share specific examples of how you communicated effectively and contributed to a team’s success, especially in interdisciplinary settings.
✨Demonstrate Your Problem-Solving Skills
Expect to face some technical challenges during the interview. Practice explaining your thought process when tackling problems, particularly those related to large-scale data processing or automation. Show how you approach issues methodically and how you’ve improved processes in previous roles.
