Researcher II, Battery Systems & Agentic AI

Location: Santa Clara, CA | Full-time, Hybrid role | Bachelor’s degree required | No sponsorship available

About the Role

With a focus on vehicle electrification, intelligent mobility, and data-driven engineering, Nissan is advancing next-generation EV platforms and battery technologies. We are committed to leveraging software, data, and system-level intelligence to maximize battery performance, safety, and lifetime value. The Battery R&D team in Silicon Valley combines battery engineering, large-scale data analytics, and artificial intelligence to enable next-generation battery intelligence.

As an SME Researcher, you will apply deep battery systems expertise to support the development of agentic AI-driven battery intelligence. This role serves as a key bridge between battery engineering, data science, and AI development, translating physical battery system behavior into intelligent decision-making frameworks.

Responsibilities

• Define battery system states: Including State of Charge (SOC), State of Health (SOH), thermal conditions, and system constraints.
• Interpret and formalize BMS logic: Capture operational boundaries and safety/control constraints within system representations.
• Translate battery behavior: Into structured computational frameworks for AI integration.
• Agentic AI Development:
  • Design AI systems capable of making autonomous battery-related decisions.
  • Define state, action, and reward structures for intelligent decision-making frameworks.
  • Incorporate physics-based constraints into AI models.
  • Support adaptive and self-optimizing battery management strategies.
• Data & Analytics:
  • Analyze large-scale vehicle and telematics datasets.
  • Identify real-world usage patterns and battery degradation behaviors.
  • Develop and utilize Python-based data analysis workflows (NumPy, Pandas, etc.).
  • Support predictive modeling and simulation environments.
• Cross-Functional Integration:
  • Collaborate closely with BMS, data science, and AI teams.
  • Align physical battery constraints with AI-based decision logic.
  • Bridge embedded battery systems and cloud-based analytics platforms.
• Strategic Contributions:
  • Contribute to Nissan’s next-generation battery intelligence roadmap.
  • Identify high-impact applications for agentic AI technologies.
  • Translate technical findings into actionable business and engineering outcomes.

Requirements

• Bachelor’s degree and 5–10 years of relevant experience; or Master’s degree and 2–5 years of relevant experience; or Ph.D. and 0–2 years of relevant experience.
• Degree in Engineering, Computer Science, or a related technical field.
• Strong understanding of battery behavior (SOC, SOH, thermal characteristics, operating constraints).
• Solid knowledge of Battery Management Systems (BMS), including estimation methods, control logic, and safety concepts.
• Proficiency with Python and data analysis tools (NumPy, Pandas, etc.).
• Experience with large-scale datasets (vehicle, telematics, IoT, or similar).
• Ability to abstract physical systems into computational/analytical frameworks.
• Strong problem-solving skills and cross-functional collaboration capabilities.

Preferred Qualifications

• Experience with artificial intelligence and machine learning technologies.
• Knowledge of reinforcement learning, agent-based systems, or sequential decision-making frameworks.
• Familiarity with vehicle/telematics data analysis.
• Experience with cloud-based data platforms.
• Background in battery degradation analysis, modeling, or lifecycle prediction.
• Experience with simulation environments, digital twins, or virtual validation frameworks.
• Demonstrated innovation through patents, publications, or product contributions.
• Experience working in research-focused or highly ambiguous environments.

Pay

Annual salary range: $134,042 – $230,590. This range represents the minimum and maximum base salary for this role. Actual compensation will reflect an employee’s unique skills, experience, education, and market norms, typically falling within the range.