Sr. Advanced Battery Control Engineer

Rivian•Hayward, CA

About The Position

Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. Join Rivian’s Battery Advanced Materials organization and develop cutting-edge battery technologies for Rivian’s upcoming vehicle platforms. Research, design, build, validate, refine, and launch battery optimization algorithms for real-world customer applications. Contribute to cross-functional efforts integrating sensors and obtaining experimental parameters to validate and tune model accuracy from material/electrode/cell/module/pack levels. Identify opportunities for existing modeling & algorithm updates to improve performance characteristics by using statistical analysis, genetic algorithm, and/or ML-based approaches ( e.g., DCFC, durability, state estimation, etc.). Digest large datasets into descriptor-based models to correlate and identify key design parameters, and generate additional test sets to feedback and improve the ML-trained model. Develop machine learning algorithms and/or mathematical models to predict cell and pack lifetime performance. Create feedback loop to inform future cell chemistry and form factor selection for optimal pack/EV attributes performance. Help integrate vehicle and driving profile data, pack state-of-health, and route planning to optimize fast-charge algorithms. Reconcile vehicle field data with cell-level aging models to monitor and refine algorithms and predictive capabilities ( e.g., end-of-life, warranty concerns, etc.). Collaborate with cross-functional teams including cell engineering, propulsion, thermal, and thermal control teams, systems teams, and vehicle integration to ensure seamless integration of BMS solutions. Travel up to 25% is anticipated, in addition to on-site development and collaborative work with experimental sensor implementation and data collection team members.

Requirements

PhD/Postdoc, MS + 2 years, BS + 3 years of industrial experience with degree in electrical engineering, mechanical engineering, chemical engineering, computer science, or related.
Hands-on coding expertise in Python for analyzing large datasets and algorithm performance, including experience with rank aggregation methods, supervised and unsupervised machine learning, numerical (linear and non-linear) optimization, and/or neural networks. Ability to design and implement algorithms into proof-of-concept cell/module/pack systems.
Strong understanding of electrochemical processes and battery materials for lithium-ion cell technology is preferred, with the ability to optimize energy, power, fast charging, cycle life, and safety for specific battery specifications.
Strong understanding of multi-scale physics modeling, thermal modeling, electrochemical modeling, and system controls using MATLAB, C++, ANSYS, COMSOL, etc.
Previous experience on P2D/ECM modeling, Simulink, COMSOL, Abaqus, Fortran, C/C++, SQL/query-based methods, image processing, CNNs, and/or graph neural networks.
Experience with state-of-the-art electrochemical models and detailed 3D cell modeling to accurately predict electrode potentials and mechanical design limitations under extreme conditions.
Experience with lithium-ion battery-related fields for consumer electronics, automotive, or cell manufacturing preferred.
Communication skills and ability to lead cross-functional efforts between diverse groups to drive projects to completion.
A deep understanding of the trends in cell development, linking from current state-of-the-art to emerging cell chemistries.
Proven research experience in next-generation battery technologies on lithium-ion batteries and beyond.

Responsibilities

Research, design, build, validate, refine, and launch battery optimization algorithms for real-world customer applications.
Contribute to cross-functional efforts integrating sensors and obtaining experimental parameters to validate and tune model accuracy from material/electrode/cell/module/pack levels.
Identify opportunities for existing modeling & algorithm updates to improve performance characteristics by using statistical analysis, genetic algorithm, and/or ML-based approaches ( e.g., DCFC, durability, state estimation, etc.).
Digest large datasets into descriptor-based models to correlate and identify key design parameters, and generate additional test sets to feedback and improve the ML-trained model.
Develop machine learning algorithms and/or mathematical models to predict cell and pack lifetime performance.
Create feedback loop to inform future cell chemistry and form factor selection for optimal pack/EV attributes performance.
Help integrate vehicle and driving profile data, pack state-of-health, and route planning to optimize fast-charge algorithms.
Reconcile vehicle field data with cell-level aging models to monitor and refine algorithms and predictive capabilities ( e.g., end-of-life, warranty concerns, etc.).
Collaborate with cross-functional teams including cell engineering, propulsion, thermal, and thermal control teams, systems teams, and vehicle integration to ensure seamless integration of BMS solutions.

Benefits

The successful candidate may be eligible for annual performance bonus and equity awards.
We offer a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a competitive portfolio of insurance benefits including life, medical, dental, vision, short-term disability insurance, and long-term disability insurance to eligible employees. You may also have the opportunity to participate in Rivian’s 401(k) Plan and Employee Stock Purchase Program if you meet certain eligibility requirements. Full-time employee coverage is effective on their first day of employment. Part-time employee coverage is effective the first of the month following 90 days of employment. More information about benefits is available at rivianbenefits.com.

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