We are seeking a Sr. Simulation Software Engineer, ADAS/AD to help scale and advance the simulation infrastructure for our autonomous driving systems.

This role is based in Newark, CA and requires employees to be onsite five days a week.

The Role:

As a Sr. Simulation Software Engineer, you will be responsible for building and scaling a high-fidelity, performant simulation platform that enables the development and validation of ADAS and Autonomous Driving (AD) features. You’ll work closely with cross-functional teams to integrate the simulation environment with the full AV software stack, deploy real-time systems, and ensure comprehensive, automated testing.

Responsibilities:

• Design and develop scalable simulation pipelines to test ADAS/AD features across a wide range of real-world and edge-case driving scenarios.

• Interface simulation systems with the full AD software stack, supporting closed-loop testing workflows such as Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL).

• Create and maintain tools for building, managing, and running simulation scenarios at scale.

• Implement robust KPIs for automated validation of vehicle behavior and system performance.

• Contribute to infrastructure and tooling to expand the reach and fidelity of Lucid’s simulation capabilities.

• Collaborate across planning, perception, and systems teams to ensure accurate integration of simulation with vehicle dynamics, sensor models, and decision-making logic.

Required Qualifications:

• BS in Computer Science, Engineering, or related field (advanced degrees preferred).

• 3+ years of relevant industry experience in simulation or software development.

• Proficient in C/C++ and Python, with strong software engineering fundamentals.

• Experience with game engines (e.g., Unreal, Unity) or automotive simulation platforms such as VTD or CarMaker.

• Strong communication skills, adaptability, and a continuous learning mindset.

Preferred Qualifications:

• Familiarity with middleware systems like ROS.

• Experience with scripting and automation (e.g., Shell, Bash).

• Understanding of descriptive configuration formats such as XML and YAML.

• Background in sensor modeling or validation.

• Experience with real-time system optimization, including threading primitives and performance tuning.

• Interest or exposure to emerging ML applications in simulation (e.g., NeRFs, LLMs).