Scientific Software Engineer

Position Overview:

Solid Power is seeking a Scientific Software Engineer to join our talented team of scientists working to revolutionize the battery industry through the development of next‐generation, all solid‐state rechargeable batteries. Solid Power is a dynamic, fast‐paced, collaborative, and innovative team environment. This role is purpose-built to ensure the long-term health, resilience, and self-sufficiency of mission-critical, internally-developed software platforms, including: an end-to-end data lineage tool (tracking cell precursor information through electrochemical cycling) and a materials informatics platform (featuring ML-based prediction of cell performance, automated EIS fitting, and Bayesian optimization workflows).

The ideal candidate is a software engineer with experience in research-to-production code translation, who has a track record of working with scientific python libraries. The role is well-suited for an individual with high attention to detail who takes pride in making systems robust, well-documented, and maintainable. Familiarity with battery systems and machine learning workflows are strongly preferred.

Job Duties:

Platform Support

• Work with Materials Informatics team members to refactor scientist-developed exploratory code into robust, modular, production-ready applications.
• Propose systems/workflows to address common user feedback for existing systems, especially managing user access, authentication flows, and frontend GUI troubleshooting.
• Triage and resolve bugs, performance issues, and user-reported problems across current and future applications.
• Implement incremental improvements and some feature requests as prioritized by the team.
• Collaborate with IT on infrastructure needs including Azure resource management, Docker container orchestration, and authentication flows.
• Stay current with relevant tooling and best practices in DevOps, site reliability engineering, and LLM-powered automation.
• Learn the underlying statistical, machine learning, and mathematical transformations employed in the applications.

Testing & Reliability

• Design and implement comprehensive automated test suites (unit, integration, and end-to-end) that run on a daily cadence to validate application health.
• Build monitoring dashboards and alerting systems that surface failures or data anomalies before they impact end users.
• Develop and maintain CI/CD pipelines that enforce quality gates on every code change.

Documentation & Knowledge Capture

• Produce thorough technical documentation for both applications, including architecture overviews, API references, deployment guides, and runbooks.
• Document internal data models, transformation logic, and integration points so that any qualified engineer could onboard and readily cont