Software Integration Engineer, Autonomy

About the role

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

Great models are only as good as the system they run on. This role sits at the boundary between the ML autonomy team's models and the on-board system, taking what the research and ML teams build and turning it into something that runs reliably, in real time, on real vehicle hardware. 42dot is looking for builders who want to own the last and hardest mile between a model that works in an offline eval and a model that works in a vehicle.

FOUNDING TEAM OPPORTUNITY

The on-board integration architecture is being defined right now, with no legacy deployment stack to retrofit. An early hire here will help shape:

- The model deployment pipeline, from training-time artifact to vehicle-ready binary

- Inference optimization strategy and tooling for the whole autonomy stack

- The interface contract standard between ML models and on-board software

Responsibilities

- Integrate ML models (end-to-end driving, VLA, world models) onto the on-board compute and runtime stack

- Optimize model inference for latency, throughput, memory, and power on embedded automotive hardware

- Build and maintain the model deployment pipeline, including conversion, validation, and rollout

- Stabilize on-vehicle ML execution by diagnosing runtime failures, timing violations, and resource contention

- Define interface contracts between ML models and the rest of the on-board software stack

- Work closely with the ML autonomy team to close the loop between deployment constraints and model design

Qualifications

- A track record of grit and ownership, with a demonstrated ability to work through ambiguous technical problems to a working solution

- Bachelor's or Master's degree in CS, EE, Robotics, or related field

- 2-4 years of experience in systems or software engineering, with some exposure to ML inference, embedded systems, or real-time software

- Experience contributing to deploying or optimizing ML models in production.

- Solid C++ and Python skills, with interest in working across the ML and systems boundary

Preferred Qualifications

- Background in autonomous driving or robotics on-board software

- Exposure to inference optimization frameworks such as TensorRT, ONNX Runtime, and automotive SoCs