My client is a fast-growing biotechnology company at the cutting edge of biological discovery. By combining a proprietary multi-billion-gene dataset with advanced AI and machine learning, they are redefining what is possible in genomics, therapeutics, nutrition, and agriculture.
Having recently announced a pre-series C round of funding, they have scaled their data asset tenfold, creating one of the world’s largest biological datasets. The team comprises biologists, engineers, ML researchers, and field scientists who collaborate across disciplines to uncover previously undiscovered biology and translate it into actionable insights.
The Role:
They’re looking for a Software Engineer who enjoys building large-scale scientific and AI workflows. You’ll report to the Software Engineering Lead and work on systems that sit at the core of the company’s research and discovery platform.
The engineering team designs and runs systems that speed up the evaluation of biological and AI models, support high-throughput scientific analysis, and embed computation into scientific decision-making. The work focuses on building internal infrastructure and tools that shape how biology is analysed, designed, and explored across the organisation.
From day one, you’ll write production code, support high-throughput pipelines, and help develop core internal tools as they evolve. The role offers real ownership and room to grow into more specialised areas as the team scales.
Key Responsibilities:
- Build and maintain data processing, inference, and analysis workflows used by scientists and machine learning teams
- Contribute to large-scale, containerised pipelines running across HPC and Kubernetes environments
- Develop internal tools such as APIs, CLIs, and dashboards to support scientific and machine learning workflows
- Improve orchestration and automation to increase reproducibility and observability across workflows
- Support performance, logging, monitoring, and reliability across distributed systems
- Work closely with Platform Engineering on infrastructure usage, GPU scheduling, and cluster stability
- Collaborate with scientists to understand research workflows and turn them into scalable, automated systems
- Take part in technical design discussions and code reviews, sharing ideas and improving engineering practices
Essentials:
- 1–5 years of commercial experience in software, data, ML, or infrastructure engineering
- Strong experience with Python and Go in production environments
- Hands-on experience building or working with distributed systems
- Experience with Docker, Kubernetes, and cloud-native development
- Familiarity with workflow orchestration tools such as Dagster, Temporal, or Airflow
- Comfortable working with Linux systems and shell scripting
- Interest in large-scale data processing, machine learning workflows, or scientific computing
- A practical, builder mindset with a focus on automation and improving systems
- Curious, collaborative, and comfortable working across technical and scientific teams
Desirables:
- 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
What they can offer in return:
- The chance to play a key role in a fast-moving and impactful field
- A supportive and collaborative work environment based in central London, with flexibility and focus on development
- Close cross-functional working with engineers, data scientists, and scientists
- Competitive salary with equity
- Comprehensive benefits including private medical cover, pension, generous time off, and enhanced parental support
- Additional benefits such as childcare support, cycle to work scheme, and income protection
Please apply ASAP for immediate consideration and a fast-moving interview process!
