Scientific developer for drug discovery

Deep Origin is a biotech startup building an operating system for science that transforms how life science research is conducted. We are hiring a Senior Scientific Developer to help build and scale the computational science layer of our drug discovery platform.

• 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
• Mentor teammates and help raise the bar for scientific software engineering across the organization

• MSc or PhD in chemistry, chemical biology, bioinformatics, computational chemistry, or a closely related field — or equivalent industry experience with a strong scientific track record
• Deep domain knowledge in at least one of: medicinal chemistry, cheminformatics, structural biology, molecular simulation, or computational drug discovery
• Excellent Python skills — you write clean, tested, production-quality code and are comfortable building libraries and APIs, not just notebooks and scripts.
• You have authored and maintained an installable Python package; publishing to PyPI is a strong plus
• You work with modern packaging and environments (`uv`, `conda`, or `pixi`)
• Linting and type checking are part of your default workflow (`ruff`, `ty`)
• You have used Marimo (or similar reactive/reproducible notebook tools) for scientific exploration, demos, or documentation
• Strong Docker skills — you are comfortable containerizing scientific Python code for production, not just running pre-built images.
• You know how to containerize Python scripts, packages, and dependencies into reliable images
• You can write multi-stage Dockerfiles for complex build pipelines
• You know how to trim image size (layer caching, slim base images, build-arg hygiene, and keeping runtime images lean)
• Experience implementing scientific algorithms and workflows end to end, from prototype to deployed, maintainable software
• AI-assisted development — you use tools like Claude Code and Cursor fluently, and you have judgment about where they accelerate you and where scientific correctness demands human review.
• Strong fundamentals in software engineering: testing, version control, debugging, and designing clear interfaces for complex scientific data
• Ability to read scientific literature and translate methods into working implementations
• Systematic problem-solving approach with a strong sense of ownership
• Ability to work both independently and collaboratively in a fast-moving startup
• Будет плюсом: Substantial experience with Julia, especially for scientific computing, molecular dynamics, or high-performance numerical work; 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); Familiarity with molecular simulation ecosystems: force fields, system preparation, alchemical free-energy methods, or MD analysis pipelines; 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.); Contributions to open-source scientific software; Experience at an early-stage startup shipping scientific product features quickly

• Health insurance for you and your family
• Additional leave days added to your annual paid time off
• Collaborating with highly experienced professionals
• Salary with equity, including stock options after probation.