1 month ago
Responsibilities
- Design end-to-end materials informatics architectures spanning data, models, and user-facing applications.
- Build data-driven models, AI-based decision-support tools, automated analysis pipelines, and web-based solutions with GUIs, 2D and 3D graphics, and cloud computing.
- Use AI coding assistants and autonomous coding agents to explore data, prototype models, build software, write tests, and produce documentation.
- Direct AI agents by decomposing materials and chemistry problems, supplying domain context and physical constraints, and iterating toward defensible results.
- Collaborate with client researchers and business leaders to solve research and product-development problems and refine solutions.
- Design AI-enabled and agentic capabilities, including retrieval over materials data, where they provide value.
- Participate in code reviews, architecture reviews, sprint planning, retrospectives, daily standups, and direct client interactions.
- Protect client data and intellectual property by using AI tools only in approved, secure configurations.
Requirements
- Deep expertise in materials science and chemistry.
- Ability to apply machine learning and scientific judgment to materials R&D problems involving small, sparse, and noisy datasets.
- Ability to design and deliver solutions spanning data, models, analysis pipelines, and user-facing applications.
- Ability to use and direct AI coding assistants and autonomous coding agents for software development and research workflows.
- Ability to collaborate with client researchers and business leaders and communicate complex technical concepts clearly.
- Ability to work responsibly with confidential client data and intellectual property.
Benefits
- Meaningful work advancing drug discovery and sustainable materials research.
- Opportunity to work with agentic AI in an automation-first company.
- Collaborative colleagues and a global culture across Austin, Cambridge, and Tokyo offices.
- Access to Enthought training programs in Python, machine learning, and scientific computing.
- Flexible hybrid work based out of the Austin office.
- Competitive compensation and benefits.
Tech Stack
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