Location(s): Santa Clara, CA

Job Schedule: Full-time, Hybrid

Education Requirement: Bachelors Degree

Sponsorship: No

A Day in the Life

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.

Key responsibilities include:

• Define battery system states, including State of Charge (SOC), State of Health (SOH), thermal conditions, and system constraints.
• Interpret and formalize BMS logic and operational boundaries.
• Capture safety and control constraints within system representations.
• Translate battery behavior into structured computational frameworks.

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 the development of 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.
• 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 the gap between 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.

Who We're Looking For

We are seeking a technically curious and innovative battery systems expert who thrives at the intersection of engineering, analytics, and artificial intelligence. The ideal candidate possesses strong Battery Management System knowledge and can translate complex battery behavior into data-driven, AI-enabled solutions.

Required:

• 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, including SOC, SOH, thermal characteristics, and operating constraints.
• Solid knowledge of Battery Management Systems (BMS), including estimation methods, control logic, and safety concepts.
• Proficiency with Python and data analysis tools such as NumPy, Pandas, and related libraries.
• Experience working with large-scale datasets, including vehicle, telematics, IoT, or similar data sources.
• Ability to abstract physical systems into computational and analytical frameworks.
• Strong problem-solving skills and cross-functional collaboration capabilities.

Desired:

• Experience with artificial intelligence and machine learning technologies.
• Knowledge of reinforcement learning, agent-based systems, or sequential decision-making frameworks.
• Familiarity with vehicle and telematics data analysis.
• Experience working 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.

Santa Clara California United States of America
Salary Range:
$134,042.00 - $230,590.00
Salary Range Estimate: Annual Salary: (Minimum to Maximum of Salary Range noted here). This compensation range represents the minimum and maximum base salary rates at Nissan for jobs assigned to this particular grade level. Please note that it is uncommon for an employee to be placed at either end of the range. Rather, an employee's actual base salary generally may fall somewhere in between and reflect the employee's unique skills, work experience, education, work location, and market norms. Additionally, pay may be based on comparisons to the base salary rates of other employees with similar backgrounds working in comparable roles.