134K–252K USD / year
Machine Learning Scientist, Generative Molecular Design (Material Discovery)
Machine learningRemote — United States
Published on 2026-10-26
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About this role
Build and productionize generative ML capabilities for material discovery, helping find compounds that meet target property profiles across competing objectives. The role combines generative modeling, reward and predictor integration, data quality, and collaboration with discovery scientists.
About the company
SandboxAQ leverages artificial intelligence and quantum technology to improve decision-making processes across various sectors, including life sciences and medical devices. The company focuses on developing innovative solutions that address complex challenges in data analysis, research, and product management.
The team
AI Simulation (AI Sim) division, within the ChemSim team.
Stack
PyTorchTensorFlowJAXGoogle Cloud PlatformAWSDiffusion modelsFlow matchingGFlowNets
What you'll do
- Architect and evolve the generative platform for material discovery.
- Implement and optimize generative models to navigate chemical space and address multi-objective optimization.
- Integrate learned predictors and simulation-derived oracles as generation rewards; formulate rewards for competing objectives.
- Develop and deploy computational workflows to generate and evaluate hypotheses.
- Identify data sources and own data ingestion, quality control, and validation.
- Collaborate with ML researchers and R&D, discovery, engineering, and product teams.
What we're looking for
- 4+ years building and shipping generative or applied ML for material discovery, in industry or equivalent research.
- PhD or MSc in computational chemistry, physics, computer science, or equivalent.
- Experience building scalable ML with PyTorch, TensorFlow, or JAX.
- Ability to write clean, maintainable, production-grade code for industry-scale applications.
- Familiarity with cloud platforms such as Google Cloud Platform or AWS.
- Experience developing ML code collaboratively using modern software engineering practices.
- Strong communication and collaboration skills.
Nice to have
- Active learning and Bayesian optimization for design-make-test loops with expensive simulation or experimental oracles.
- Guided generative modeling, constrained generation, inference-time steering, or model adaptation for material discovery.
- Agentic or LLM approaches to material discovery.
Benefits
- Equity and performance-based incentives.
- Health, dental, and vision insurance.
- 401(k) with company match.
- Generous parental leave.
- Flexible hybrid arrangements and generous PTO.
- Learning budget and career-development opportunities.