Staff Battery Algorithms Engineer
| Company | Tesla |
|---|---|
| Location | Palo Alto, CA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Senior, Expert or higher |
Requirements
- Degree in Mechanical Engineering, Electrical Engineering, Materials Science, or equivalent in experience and evidence of exceptional ability
- Excellent understanding of the electrochemical physics of lithium-ion cells, and experience with modeling these physics across a range of fidelities
- Excellent ability to ground-up design, analyze, and implement, and ship battery management algorithms in real-world applications, demonstrated by strong examples
- Strong understanding of battery diagnostics and anomaly detection algorithms
- Strong evidence of ability to champion large cross-functional technical initiatives in the battery algorithms field, requiring collaboration with functions like cell modeling, cell reliability, and suppliers
- Strong evidence of ability to execute on open-ended projects in a fast-paced, resource-constrained environment
- Basic proficiency with embedded software development in C/C++
Responsibilities
- Own a subset of the algorithms that run on the BMS in millions of Tesla vehicles worldwide – these algorithms span cell capacity, impedance, energy, fast charging, and degradation estimation to diagnostics of sensors, cells, and battery pack components
- Identify opportunities where BMS algorithm advancements can materially impact our product standing on metrics like fast-charging, energy estimation accuracy, SOH, etc.
- Actively drive cross-functional efforts with cell modeling, cell qualification, firmware, teams to advance state-of-the-art of BMS algorithms in fast charging, energy, and health estimation
- Work with cross functional teams to take the fastest path to execute battery algorithm advancements across multiple cell programs through various parts of their life cycle
- Research, design, prototype, and prove functionality of novel battery estimation and control approaches to maximally extract performance from our cells
- Build fleet data pipelines and tools to monitor performance of your algorithms in our fleet, identify areas for improvement, and test efficacy of proposed solutions
Preferred Qualifications
- Experience productionizing physics model-based fast-charging algorithms, preferred