Solid Power is hiring a Scientific Software Engineer to ensure the long-term health, resilience, and self-sufficiency of our mission-critical, internally-developed software platforms, including an end-to-end data lineage tool and a materials informatics platform. In this role, you will be instrumental in translating scientific code to production, supporting platforms, and building reliable, automated systems.

What You'll Do

Work with Materials Informatics team members to refactor scientist-developed exploratory code into robust, modular, production-ready applications.
Propose systems and 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 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.
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.
Produce thorough technical documentation, including architecture overviews, API references, deployment guides, and runbooks.
Document internal data models, transformation logic, and integration points to facilitate team onboarding.
Maintain living documentation that evolves alongside the codebase and automate documentation generation where feasible.
Design and implement agentic (AI-assisted or rule-based) workflows capable of detecting, diagnosing, and resolving routine application issues autonomously.
Build self-healing mechanisms for common failure modes, including automated rollback, retry logic, and environment recovery.
Continuously expand the scope of issues that can be resolved autonomously to reduce on-call burden.
Serve as the primary liaison between the Materials Informatics team and data-generating teams.
Define, negotiate, and enforce data contracts that specify schema, format, quality, and delivery expectations for upstream data sources.
Monitor incoming data for contract violations and work with source teams to resolve discrepancies promptly.

What We're Looking For

Bachelor’s degree or beyond in Computer Science, Software Engineering, Information Technology, Data Science, Applied Mathematics/Statistics, or a related field.
Minimum of 3 years of professional experience in software engineering, DevOps, site reliability engineering, or a closely related role.
Significant experience translating scientist-developed exploratory code to robust, modular, production-ready applications.
Strong command of Python, including experience with testing frameworks (pytest, unittest), scripting, and automation.
Understanding of the Torch framework with demonstrated experience supporting applications that make use of it.
Familiarity with ML model serving, retraining pipelines, or MLOps tooling.
Working knowledge of JavaScript/TypeScript and modern front-end frameworks relevant to maintaining existing applications.
Hands-on experience with Docker for building, deploying, and managing containerized applications.
Familiarity with Azure Active Directory (authentication), Azure Databricks, Azure Data Factory, DuckDB, and related cloud infrastructure.
Demonstrated experience building automated test pipelines and continuous integration/delivery workflows (e.g., GitHub Actions, Azure DevOps, Jenkins).
Proven ability to write clear, comprehensive technical documentation for diverse audiences (developers, scientists, end users).
Strong interpersonal skills with the ability to negotiate data contracts and work effectively across technical and scientific teams.
Thrives in a fast-paced startup environment with minimal daily supervision; proactively identifies problems and implements solutions.

Nice to Have

Familiarity with battery systems and machine learning workflows.
Relevant certifications (e.g., Azure Developer Associate, Azure DevOps Engineer Expert).
Experience building or integrating agentic AI workflows (e.g., using LLM-based agents for automated troubleshooting, code repair, or DevOps tasks).
Familiarity with data pipeline and workflow orchestration tools (e.g., Apache Airflow, Prefect, Dagster).

Technical Stack

Languages & Frameworks: Python, Torch, JavaScript, TypeScript, pytest, unittest
Infrastructure & Tools: Docker, Azure Active Directory, Azure Databricks, Azure Data Factory, DuckDB, GitHub Actions, Azure DevOps, Jenkins

Team & Environment

You will collaborate closely with the Materials Informatics team and IT, operating within a dynamic, fast-paced, collaborative, and innovative team environment.