Senior Simulation Engineer

Parallel Domain is accelerating the development of autonomous systems through high-fidelity simulation and synthetic data generation. Our platform produces labeled sensor data—including camera, lidar, and radar streams—that helps teams train, test, and validate machine learning models at scale. With capabilities for both open- and closed-loop simulation, our tools provide configurable, realistic environments that unlock faster, safer iteration for AI systems.
We support a wide range of industries, including automotive, aerial robotics, agriculture, warehouse logistics, and security. Our customers—such as the Toyota Research Institute and Woven Planet—use Parallel Domain to improve perception systems and bridge the gap between simulation and the real world. Whether enabling virtual testing of autonomous vehicles or generating diverse edge-case scenarios for robotics, we help customers build smarter AI, faster.
About this role:
We are looking for a Senior Simulation Engineer with deep expertise in C++, Python and Unreal Engine 5.x to contribute to the development of core simulation features and systems. In this role, you’ll be a senior contributor in the Simulation team in designing scalable simulation infrastructure and driving initiatives that improve the realism, accuracy, and usability of our virtual environments. You’ll have significant ownership across the codebase and collaborate closely with product, machine learning, and content teams to bring highly performant and configurable simulation to life.
This position is open to candidates based in the **Pacific Northwest**, specifically in or near **Vancouver, BC or Seattle, WA**. We support hybrid or remote work for team members in this region.

Responsibilities

• Architect and implement simulation frameworks in Unreal Engine 5.x, with a focus on physical realism and runtime performance
• Develop and maintain critical systems for vehicle dynamics, physics-based interaction, and environment behaviors
• Build Python tooling and interfaces to support automated workflows, scenario generation, and simulation control
• Partner with internal stakeholders to scope technical requirements, mentor team members, and review design decisions
• Identify and resolve performance bottlenecks in simulation systems across CPU/GPU domains
• Contribute to technical strategy for scaling simulation systems across diverse use cases and customer domains

Qualifications

• 5+ years of professional experience in software development, simulation, or game engine programming
• Expert-level knowledge of C++ (modern standards) and solid professional experience with Python
• Extensive hands-on experience with Unreal Engine 5.x, including performance tuning and engine-level development
• Proven experience building and optimizing complex, real-time simulation systems
• Strong understanding of physics systems, including rigid body dynamics and kinematic modeling
• Excellent communication skills, with the ability to work cross-functionally and mentor peers

Nice to Have (Bonus Points)

• Familiarity with PhysX or other real-time physics SDKs
• Experience with vehicle simulation, including drivetrain, suspension, or terrain interaction modeling
• Exposure to semantic simulation, labeling systems, or synthetic data generation workflows
• Background in game development or simulation platforms focused on tooling and infrastructure

Why Join Parallel Domain
* Shape the core simulation technology powering the next generation of autonomous systems* Join a talented, mission-driven team working at the intersection of machine learning, simulation, and robotics* Make a direct impact on real-world products used in automotive, robotics, and AI development
Benefits
* Competitive salary and equity packages* Full health, dental, and vision coverage* Flexible, remote-friendly work culture within the Pacific Northwest* Learning stipends and professional development opportunities* Generous vacation and company-wide breaks
Join us in building the world’s most advanced simulation platform. Apply now and help define the future of synthetic data.