Senior Scientific Developer

Responsibilities

• Design, implement, and maintain scientific software for drug discovery: docking, free-energy perturbation (FEP), molecular dynamics, cheminformatics, and related computational biology workflows.
• Build and evolve the Deep Origin Python SDK and scientific APIs that scientists use to run, monitor, and analyze platform workflows.
• Develop and maintain Julia-based simulation and analysis packages for molecular systems (e.g., system preparation, MD/FEP protocols, post-processing).
• Author and operate workflow definitions (Argo Workflows) and serverless scientific services (Knative) that orchestrate multi-step computational pipelines on Kubernetes.
• Package scientific tools as reproducible, containerized functions with clear input/output contracts, validation, and integration tests.
• Translate domain requirements from medicinal chemistry and computational biology into robust, scalable technical implementations.
• Collaborate with platform engineering, product, and science teams to integrate scientific tools into our multi-tenant cloud platform.
• Ensure scientific code quality through testing, documentation, benchmarking, and careful handling of edge cases in real-world molecular datasets.
• Debug and resolve issues across the full stack — from numerical methods and force-field behavior to workflow failures in production.
• Contribute to engineering best practices: code review, CI/CD, observability, and operational runbooks for long-running scientific jobs.

Languages

Required

• Excellent Python skills — you write clean, tested, production-quality code and are comfortable building libraries and APIs, not just notebooks and scripts. In practice, that means
• Strong Docker skills — you are comfortable containerizing scientific Python code for production, not just running pre-built images. In practice, that means
• Experience implementing scientific algorithms and workflows end to end, from prototype to deployed, maintainable software.
• Hands-on experience with Kubernetes and cloud-native deployment patterns.
• Experience with Argo Workflows (or similar workflow orchestration) and Knative (or similar serverless/container platforms).

Nice to have

• You have used Marimo (or similar reactive/reproducible notebook tools) for scientific exploration, demos, or documentation.
• Systematic problem-solving approach with a strong sense of ownership.
• TypeScript/JavaScript experience — our platform UI and some backend services use modern web stacks.
• Experience with PostgreSQL or scientific data platforms (chemical structure storage, RDKit, etc.).