Software Engineer, Robotics Simulation & AI Infrastructure

Qualcomm•San Diego, CA

4d•$148,300 - $222,500

About The Position

Qualcomm Advanced Robotics Team is building the AI-first stack for the next generation of general-purpose robots, including AMRs, cobots, and emerging humanoids. They combine heterogeneous compute (CPU/GPU/DSP/NPU) with a full Robotics SDK, an integrated simulation platform, and AI operations infrastructure. Their high-performance robotics SoCs enable on-device, edge, and scaled cloud workloads. This team specifically builds AI infrastructure for modern robotics, with a focus on a best-in-class, in-house simulation platform. This platform is built on high-performance computing and open data formats like OpenUSD, URDF, and glTF, and is tuned for Qualcomm robotics SoCs and the robots they power. The robots encompass various applications such as tabletop manipulation, legged locomotion, navigation and vision, and long-horizon tasks requiring multi-stage reasoning. This is a ground-up software effort focusing on runtime architecture, performance, clean APIs, and developer tooling. The simulator is a key component, running on both developer workstations and cloud compute, supporting interactive authoring, vectorized GPU environments locally, and orchestrated headless jobs at scale in the cloud. It's used for training policies, generating synthetic data for robot foundation models, gating software releases in CI, and running hardware-in-the-loop tests. The role involves close collaboration with Qualcomm's AI operations workstreams, with a significant portion of engineering focused on how simulation integrates into training, dataset, and deployment infrastructure.

Requirements

Strong software engineering fundamentals, with production code experience.
Working grasp of simulation as applied to robotics (rigid-body dynamics, kinematics, coordinate frames, numerical integration, sensor models) or the aptitude to build one quickly.
Ability to pick up unfamiliar stacks quickly and independently.
Effective use of modern AI tooling in development.
Comfort working with a mix of settled and validated design layers.
Motivation by physical AI and robots that function.
Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.
OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience.
OR PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.

Nice To Haves

Robotics simulators or real-time 3D engines (MuJoCo/MJX, Isaac Sim or Isaac Lab, Newton, PhysX, Gazebo, Drake, Unreal Engine, or Unity).
GPU programming and performance engineering (CUDA, Warp, Vulkan, compute shaders, or accelerator-aware data layout).
Machine learning for robotics (reinforcement learning at scale, imitation learning, robot foundation models and VLAs, PyTorch).
3D graphics and scene pipelines (OpenUSD composition, PBR materials, real-time and offline rendering, sensor simulation).
Robotics and distributed systems (ROS 2/DDS, URDF/MJCF, pub/sub and schema-driven wire formats, containers, cluster orchestration, and CI/CD for compute-heavy workloads).
Working fluency in AI infrastructure around simulation (Kubernetes-native workflow orchestration, containerized GPU jobs, config-driven experiment frameworks, checkpoint/resume semantics, experiment tracking).
Effective leverage of modern AI tools (frontier AI models, AI coding and agentic workflows, AI-assisted learning, building knowledge bases).

Responsibilities

Design and build core simulator subsystems — scene representation and authoring, physics backends, sensor models, rendering — across various robot scenarios.
Profile and optimize simulation and training workloads for high throughput.
Integrate and extend best-in-class open engines for physics and rendering.
Build high-throughput paths for policy training and synthetic data generation.
Make simulation a release gate by implementing scenario suites, deterministic replay, benchmarks, and metrics.
Enable continuous learning workflows by integrating simulation into training, evaluation, and deployment pipelines.
Set up hardware-in-the-loop configurations to quantify simulation-to-reality divergence.
Partner with AI operations, perception, controls, and silicon teams, owning design docs, reviews, tests, and APIs.
Perform code reviews and regression tests; triage and fix issues.
Collaborate with hardware, systems, test, and AI operations teams.
Write and review technical documentation, design rationale, and knowledge-sharing material.