About the Role

We are building next-generation vehicle intelligence at 42dot, enabling vehicles to understand user intent, trip context, vehicle state, environmental conditions, and system constraints, then coordinate vehicle behavior to deliver personalized, proactive, transparent, and trustworthy experiences.

As an ML / AI Engineer, you will design and develop AI-driven vehicle intelligence features that help users drive farther, feel more confident, reduce cognitive load, and experience vehicles that adapt to their needs. You will work across vehicle telemetry, user behavior, navigation, energy usage, thermal systems, cabin comfort, charging, simulation, and fleet data to build intelligent systems that can predict, recommend, plan, and optimize vehicle behavior.

This role is focused on applying modern AI and machine learning technologies, including reinforcement learning, multimodal AI, foundation models, large language models, personalization, time-series forecasting, planning, simulation-based learning, and on-device inference. Reinforcement learning will be an important intelligence algorithm for developing adaptive vehicle behaviors, optimizing system-level decisions, and improving vehicle experiences through simulation, fleet feedback, and real-world operating data.

You will also collaborate closely with autonomous driving and VLA engineers to connect, integrate, and combine vehicle intelligence with driving intelligence. This role is not focused on developing core VLA models, but it will help define how user intent, trip goals, vehicle constraints, energy targets, comfort preferences, and system-level recommendations are shared with VLA and autonomous driving systems.

Responsibilities

• Develop AI-powered vehicle intelligence features that understand user intent, trip goals, vehicle state, and system constraints.
• Apply reinforcement learning, planning, optimization, and data-driven modeling to improve vehicle-level decisions across energy, comfort, charging, routing, and proactive vehicle preparation.
• Build models using vehicle telemetry, navigation data, user behavior, weather, traffic, cabin conditions, charging patterns, and fleet data.
• Create personalization models that learn user routines, comfort preferences, driving patterns, charging habits, and trip priorities while preserving privacy and user control.
• Use simulation, digital twins, and scenario-based testing to train, evaluate, and validate AI behavior before production deployment.
• Collaborate with autonomous driving and VLA teams to define interfaces for sharing user intent, route objectives, vehicle constraints, energy targets, comfort preferences, and system-level recommendations.
• Integrate ML models into production vehicle and cloud platforms, considering latency, compute efficiency, reliability, safety, explainability, and over-the-air update readiness.
  Work cross-functionally with Product, UX, Systems Engineering and Controls.

Qualifications

• Bachelor's, Master's, or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Electrical Engineering, or a related technical field.
• 3+ years of experience developing and deploying machine learning or AI solutions in production environments.
• Strong experience with machine learning frameworks and programming languages such as Python, PyTorch, TensorFlow, JAX, or similar tools.
• Experience building predictive, optimization, recommendation, forecasting, or personalization models using large-scale real-world datasets.
• Solid understanding of reinforcement learning, time-series modeling, statistical learning, and data-driven decision-making systems.

Preferred Qualifications

• Experience applying reinforcement learning, planning, simulation-based learning, or optimization techniques to complex real-world systems.
• Familiarity with vehicle systems, connected vehicles, mobility platforms, automotive software, energy management, charging systems, or intelligent transportation technologies.
• Experience working with multimodal AI, foundation models, large language models (LLMs), agent-based systems, or personalized AI experiences.
• Knowledge of deploying ML models to edge or embedded platforms, including considerations for latency, compute efficiency, safety, reliability, and on-device inference.
• Experience collaborating with cross-functional teams including product, UX, controls, systems engineering, autonomous driving, or robotics organizations to bring AI-powered features into production.

Interview Process

• Application Review - Coding Test - 1st interview - 2nd interview - Offer Negotiation - Hiring
• The screening procedures may vary depending on the position, schedule, or other circumstances.
  You will be individually notified of the screening schedule and results via the email address provided in your application.

Compensation

• $189,000 -$261,000/year

Additional Information

• In accordance with fair hiring practices, do not include any personal information unrelated to your job qualifications (e.g., Social Security Number, family relations, marital status, age, photo, physical condition, place of birth, etc.) in your resume.
• All documents must be submitted in PDF format and under 30MB in size.
• If you experience issues uploading your resume, please send it along with the job posting URL to [redacted].
• We strongly encourage applications from U.S. veterans and candidates eligible for employment preference under applicable laws.

※ Please review the following information before applying.

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