ML​/AI Engineer - Vehicle Intelligence

We are looking for the best

About Us

42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of mobility by advancing the development of software-defined vehicles.

We develop safety-first, user-centric software-defined vehicle technologies that deliver the latest performance through continuous updates like smartphones. By advancing software and AI technology, 42dot envisions a world where everything is connected and moves autonomously through a self-managing urban transportation operating system.

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, Tensor Flow, 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,…