Chemix is seeking a highly-motivated battery modeling engineer to develop and expand our AI platform for battery materials discovery. Our AI platform is the core of Chemix. Though data is first and foremost in any application of AI, it is typically very scarce in materials development. We've designed our R&D operation to generate large, high-quality battery materials datasets. As a battery modeling engineer at Chemix, you'll be responsible for both the integration of physics-based battery models (DFN, ECM, thermal, etc.) into our automated experimental design pipeline at scale and the use of these models to address specific customer needs. You'll make a fundamental contribution to developing the batteries that will power the electrification revolution in transportation and beyond.
As an early employee at a fast-moving startup, we expect you to quickly and creatively solve all kinds of technical problems, including those beyond your core expertise. An ideal candidate is able to learn quickly, is eager to stretch their knowledge of the battery software stack, takes pride in the quality of their work, and wants to make a real impact in energy storage technologies for electric transportation.
Responsibilities:
Integrate physics-based battery models into our automated experimental design pipeline at scale
Develop physics-based battery models of our commercial-format cells in python or using COMSOL to address specific customer requests
Contribute code to Chemix's internal codebase (Python)
Interface with our machine learning scientists, battery engineers, and customers
Inform the optimization of the R&D process that generates our data
Requirements
PhD in battery modeling or similar, or significant previous experience
Extensive experience with python and standard battery modeling frameworks, e.g. PyBaMM, COMSOL
Experience with the fundamentals of data science and software ops: git, testing, CI/CD
Clear communication and good people skills
Strong organization and ability to manage parallel projects
Nice to have:
Experience with python software development best practices
Experience with workflow orchestration tools, e.g. Airflow, Prefect, Luigi, Dagster, and scaling tools such as Dask
Experience with machine learning and data science / statistics techniques
Experience with cloud web services (AWS, Google Cloud, Azure, etc.), Docker, Kubernetes
Familiarity with experimental battery chemistry/materials science
Benefits
Stock Option Plan
Health Care Plan (Medical, Dental & Vision)
Retirement Plan (401k)
Paid Time Off (Vacation, Sick & Public Holidays)
Family Leave (Maternity, Paternity)