In This Role, You Will Be Responsible For...
- Build highly performant, multithreaded systems that scale across CPU and GPU.
- Design memory-efficient data pipelines capable of supporting large-scale procedural environments and densely populated simulation scenes.
- Work with our Senior Architect to implement core engine systems for real-time simulation, rendering, and sensor data output.
- Work with the team to design profiling and debugging tools to monitor engine health, frame timing, memory usage, and system latency.
- Optimize GPU compute workloads to ensure high throughput for both graphical rendering and sensor-specific data generation.
- Develop tooling and metrics pipelines to continuously monitor and improve system bottlenecks.
- Collaborate closely with simulation, graphics, and infrastructure teams to ensure platform performance meets the demands of parallelized, distributed workloads.
- Your work will be central to enabling high-volume, high-fidelity synthetic data generation, powering cutting-edge autonomy and machine perception systems in production and research environments.
Qualifications...
- 5–8+ years of experience in graphics or engine development, preferably within real-time or simulation-focused environments.
- Deep proficiency in C++ and Game Engine Development.
- Strong background in: Multithreaded engine architecture, Performance profiling (CPU/GPU, frame timing, load balancing), GPU compute and pipeline optimization, Shader development (e.g., HLSL, GLSL), Low-level memory management
- Familiarity with real-time 3D math, spatial data structures, and camera/sensor modeling.
- Experience driving technical projects from architecture through to deployment, with strong collaboration across cross-functional teams.
- Shipped a AAA photorealistic game, with involvement in performance-critical systems.
- Worked with Unreal Engine 4.x or newer, or another modern high-performance game engine.
- Hands-on experience working directly with the core engine codebase, not just using the editor or scripting layer. e.g., modifying rendering systems, memory allocators, or runtime infrastructure.
Bonus Points...
- Experience with simulation systems for autonomous vehicles, robotics, or related domains
- Familiarity with sensor simulation technologies such as LiDAR, Radar, depth, and realistic simulation of a broad range of cameras with varying levels of quality and their image signal processing capabilities.
- Prior work on deterministic simulation, fixed update loops, or real-time physics systems.
- Experience scaling and optimizing engines for cloud or distributed compute environments
- Knowledge of large-scale procedural content generation and streaming world systems
- Exposure to AI/ML-based content and rendering methods such as NeRFs and Gaussian Splatting
- Experience collaborating with technical artists, 3D artists, and product designers
- Led roadmap planning, authored technical design documentation, and defined engineering milestones
What Success Looks Like
- You’ve profiled and begun optimizing key engine subsystems for performance and efficiency
- You've owned and delivered core components within the rendering infrastructure that enable larger and more performant scenes, helped guide best practices for data authoring and packaging, and become the teams go-to for performance vs quality tradeoffs in the pipeline.
Top Skills
What We Do
Training and testing autonomous systems in the real world is a slow, expensive and cumbersome process. Parallel Domain is the smartest way to prepare both your machines and human operators for the real world, while minimizing the time and miles spent there. Connect to the Parallel Domain API and tap into the power of synthetic data to accelerate your autonomous system development.
Parallel Domain works with perception, machine learning, data operations, and simulation teams at autonomous systems companies, from autonomous vehicles to delivery drones. Our platform generates synthetic labeled data sets, simulation worlds, and controllable sensor feeds so they can develop, train, and test their algorithms safely before putting these systems into the real word.
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