
Principal Software Engineer
- London
- Permanent
- Full-time
- Write and deliver production-grade features and infrastructure in Python (FastAPI, PostgreSQL) and JavaScript/React, Tailwind, Next.js. Review and debug code written by the team.
- Drives decisions that influence the product design, application functionality, and technical operations and processes.
- Plan and implement architectures for high-performance protein engineering pipelines.
- Translate ambitious product and research requirements into clear technical milestones.
- Operationalize AI workflows from research prototypes into reproducible, production-ready systems used daily by scientists.
- Build intelligent interfaces that integrate agentic tools into scientists’ workflows.
- Develop seamless full-stack integrations between AI agents, user inputs, and large-scale biological datasets.
- Deploy and maintain containerized workflows (Docker, Kubernetes) in cloud-native environments (GCP preferred).
- Create high-throughput data integration pipelines connecting experimental, computational, and AI-driven outputs.
- Design and implement systems to capture and process wet lab experimental data in near real-time.
- Incorporate this feedback into computational models and protein design workflows, enabling rapid iteration through the DBTL cycle.
- Collaborate closely with wet lab scientists to ensure data quality, interoperability, and traceability across the platform.
- Conduct code reviews, share best practices, and help engineers solve complex technical challenges.
- Contribute to project planning to ensure timelines are realistic and delivery is on track.
- Proven track record of shipping production systems in Python (FastAPI, PostgreSQL) and JavaScript/TypeScript (React, Next.js).
- Strong background in distributed systems, scalable architectures, and data-intensive applications.
- Experience deploying ML models into production, ideally with multi-agent orchestration (LangGraph, Pydantic, or custom frameworks).
- Familiarity with MLOps best practices, model lifecycle management, and CI/CD workflows.
- Experience designing data systems that bridge laboratory instrumentation and computational workflows.
- Familiarity with handling experimental datasets, scientific data formats, and lab data management systems is a plus.
- Bonus: experience with computational biology, protein engineering, or biotech data workflows.
We are sorry but this recruiter does not accept applications from abroad.