Senior Scientific Developer

Deep Origin

Senior Scientific Developer at Deep Origin building and scaling computational tools for drug discovery with a focus on scientific computing and software engineering.

Posted 7/9/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in scientific software development for drug discovery, with strong capabilities in Python and Docker for building and maintaining scalable, production-quality applications. Proficient in translating complex scientific requirements into robust technical implementations while ensuring code quality and collaboration across multidisciplinary teams.

Highest-signal resume keywords

Python ProgrammingDocker ContainerizationScientific Software DevelopmentKubernetes DeploymentMolecular Simulation

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Applicant Tracking System Keywords

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Hard Skills

Scientific Software DevelopmentPython ProgrammingDocker ContainerizationJulia ProgrammingArgo WorkflowsKnativeMolecular DynamicsCheminformaticsFree-Energy PerturbationComputational Drug Discovery

Soft Skills

Problem-SolvingCollaborationMentoringOwnershipCommunication

Tools & Technologies

KubernetesCI/CDMarimoUvCondaPixiRuffTyClaude CodeCursor

Industry Keywords

Medicinal ChemistryComputational BiologyBioinformaticsChemical BiologyScientific Algorithms

Tech Stack

Tools & technologies

CloudDockerKubernetesPython

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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. In practice, that means:
- 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. In practice, that means:
- 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.
**Preferred: **
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.

Benefits

Comp & perks

**What we offer**
- Opportunity to shape the future of health, longevity, and our ability to simulate life.
- Competitive compensation package with meaningful equity.
- Comprehensive health, dental, and vision coverage.
- Annual team gatherings and company events.
- Free lunch, snacks, beverages, and onsite gym access (for in-office employees).