{"id":714930,"url":"https://alion.io/job/qualcomm-software-engineer-robotics-simulation-ai-infrastructure","title":"Software Engineer, Robotics Simulation & AI Infrastructure","company":{"id":94,"name":"Qualcomm","domain":"qualcomm.com","url":"https://alion.io/company/qualcomm","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":74,"open_postings":1170,"ghost_share":0.193,"stale_share":0.106,"repost_share":0.449,"time_to_fill_p50_days":75,"computed_at":"2026-10-10T05:45:15Z"}},"role":"Robotics","role_family":"Robotics","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Diego, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":155400,"max":233200,"currency":"USD","period":"year","gross":null,"usd_annual":233200},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Argo Workflows","optional":false},{"name":"Kubernetes","optional":false},{"name":"Physical AI","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"CI/CD","optional":true},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"Digital Twin","optional":true},{"name":"Drake","optional":true},{"name":"Gazebo","optional":true},{"name":"Imitation Learning","optional":true},{"name":"Isaac Lab","optional":true},{"name":"Isaac Sim","optional":true},{"name":"Machine Learning","optional":true},{"name":"MuJoCo","optional":true},{"name":"NVIDIA PhysX","optional":true},{"name":"PyTorch","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Rest-Assured","optional":true},{"name":"ROS","optional":true},{"name":"TensorBoard","optional":true},{"name":"Unity","optional":true},{"name":"Unreal Engine","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-08-31T00:00:00Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-10-11T00:43:29Z","board_verified":true,"closed_at":null,"days_open":41,"trust":{"level":"ok","repost_count":2,"flags":["repost"],"days_open":40},"description":"Company:\nQualcomm Technologies, Inc.Job Area:\nEngineering Group, Engineering Group > Multimedia SystemsGeneral Summary:\n About Qualcomm Robotics\nThe Qualcomm Advanced Robotics Team is building the AI-first stack for the next generation of general-purpose robots - from AMRs and cobots to emerging humanoids - pairing heterogeneous compute (CPU/GPU/DSP/NPU) with a full Robotics SDK, an integrated simulation platform, and AI operations infrastructure. With our high-performance robotics SoCs, workloads that previously required the cloud now run on-device, at the edge, at scale.\nAbout This Team\nWe buildAI infrastructure for modern robotics. The simulator is a central tool in it: a modern platform developed in-house, built on high-performance computing and open data formats such asOpenUSD, URDF, andglTF, intended to be best-in-class, and tuned for Qualcomm robotics SoCs and the robots they run on. The robots span the field - tabletop manipulation with robotic arms, legged locomotion, navigation and vision, and long-horizon tasks that carry a robot through multi-stage scenes requiring reasoning over many steps.This is a ground-up software effort: runtime architecture, performance, clean APIs, and developer tooling.\nThe simulator is a first-class target on both developer workstations and cloud compute - interactive authoring and vectorized GPU environments locally, orchestrated headless jobs at scale in the cloud. We use it to train policies, generate synthetic data for robot foundation models, gate software releases in CI, run hardware-in-the-loop against real silicon, and close the sim-to-real gap on deployed robots. We deliver jointly with Qualcomm’s AI operations workstreams, and a meaningful slice of the engineering sits where simulation plugs into the training, dataset, and deployment infrastructure they own.\nThe Opportunity\nYou will own significant pieces of a simulation platform on the critical path of Qualcomm’s robotics products - the runtime and its abstractions, the physics and renderingintegrations, the training and data-generation paths, the HIL and CI plumbing, or the developer experience that makes all of it usable by robotics engineers who are not simulation specialists - across robot arms, legged platforms, and mobile robots.\nThis is first and foremost a simulation role - roughly 80% of the work is the simulator itself as a critical platform component. The remaining ~20% is AI infrastructure enablement: integrating simulation into the training, dataset, and deployment pipelines owned by the AI operations team, working with them rather than replacing them.\nThe role is open from mid-level through senior/staff, roughly 2 to 10+ years. We calibrate level from your depth during the interview loop, so apply if you are anywhere in that range.\nWhat You’ll Do\nDesign and build core simulator subsystems - scene representation and authoring, physics backends, sensor models, rendering - across the scenarios our robots work in: tabletop manipulation, legged locomotion, navigation and vision, and long-horizon, multi-stage tasks.\n\nProfile and optimize simulation and training workloads - physics solvers, rendering, data pipelines - so interactive workstation sessions and training throughput at scale stay high.\n\nIntegrate and extend best-in-class open engines - GPU physics, USD/Hydra rendering, offline path tracing - behind clean, swappable interfaces.\n\nBuild high-throughput paths for policy training and synthetic data generation: vectorized environments, GPU-resident state, procedural scene variation, domain randomization, and ground-truth labeling - consistent from an interactive workstation session to scheduled headless runs in the cloud.\n\nMake simulation a release gate: scenario suites, deterministic replay, benchmarks, and metrics that catch regressions before they reach hardware.\n\nEnable the continuous learning workflows that simulation feeds into, in partnership with the AI operations team: make simulation a first-class, config-driven stage in collect-merge-train-eval pipelines with reproducible run manifests and artifacts, so the same experiment runs locally and as containerized GPU jobs (Argo Workflows on Kubernetes) without the sim engineer owning the surrounding infrastructure.\n\nStand up hardware-in-the-loop configurations: production robotics software running on Qualcomm silicon in communication with the simulator on the host workstation or cloud, and quantify where simulation and reality diverge - system identification, contact and actuator modeling, sensor noise, measured transfer results.\n\nPartner with AI operations, perception, controls, and silicon teams, and own the design docs, reviews, tests, and APIs others depend on.\n\nWho We Look For\nStrong software engineering fundamentals, with production code you can talk through in depth.\n\nA working grasp of simulation as applied to robotics: rigid-body dynamics, kinematics, coordinate frames, numerical integration, or sensor models.- or the aptitude to build one quickly; engineers from games, graphics, and ML infrastructure backgrounds ramp well here.\n\nYou pick up unfamiliar stacks quickly and independently, and you use modern AI tooling effectively in your development loop.\n\nYou would rather converge two half-parallel code paths than add a third, and you are comfortable where some layers are settled design-of-record and others are still being validated.\n\nYou are motivated by physical AI: work that ends in a robot that functions, not only a benchmark number.\n\nMinimum Qualifications:\n• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.OR\nMaster's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience.\nOR\nPhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.\n Preferred Qualifications:\nDepth in two or three of these matters more than familiarity with all of them.If you're excited about this role but don't match every qualification, we encourage you to apply.\nRobotics simulators or real-time 3D engines: MuJoCo/MJX, Isaac Sim or Isaac Lab, Newton, PhysX, Gazebo, Drake, Unreal Engine, or Unity. Candidates from games, VFX, or AV simulation are welcome - these skills transfer directly.\n\nGPU programming and performance engineering: CUDA, Warp, Vulkan, compute shaders, or accelerator-aware data layout.\n\nMachine learning for robotics: reinforcement learning at scale, imitation learning, robot foundation models and VLAs, PyTorch.\n\n3D graphics and scene pipelines: OpenUSD composition, PBR materials, real-time and offline rendering, sensor simulation.\n\nRobotics 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.\n\nWorking fluency in the AI infrastructure around simulation - enough to integrate with it and partner effectively with the AI operations team (who own it), not to run it yourself: Kubernetes-native workflow orchestration (Argo Workflows or similar),containerized GPU jobs and shared-storage datalakes (NFS/PVC volumes), config-driven experiment frameworks, checkpoint/resume semantics, and experiment tracking (TensorBoard, Weights & Biases).\n\nEffective leverage of modern AI tools as a core engineering skill:frontier AI models, AI coding and agentic workflows, AI-assisted learning for ramping on new domains quickly, and building knowledge bases - personal or team - to accelerate on-boarding and future work.\n\nPrincipal Duties and Responsibilities\nDesigns, develops, integrates, and validates the in-house simulation platform and its tooling, targeting Qualcomm robotics SoCs and robots across form factors and scenarios - tabletop manipulation, legged locomotion, navigation and vision, and long-horizon multi-stage tasks.\n\nSpecifies environments, tasks, and evaluation criteria for training and assessing robot policies; partners directly with robotics researchers and engineers to iterate on tooling, workflow, and model performance until results hold up on real hardware.\n\nBuilds digital twins of robots, sensors, and work cells, and hardware-in-the-loop configurations where the robotics stack runs on Qualcomm silicon against a simulator on the host, so algorithms are developed and validated before hardware time is committed.\n\nBuilds synthetic data pipelines for robot foundation models and task models, and simulation-based regression suites that gate software releases.\n\nWorks with the in-house sensor teams to model sensor behavior from real characterization and calibration data, delivering high-fidelity sensor simulation modules.\n\nWorks closely with the robotics AI infrastructure team to complete the data flywheel: a continuous learning pipeline spanning data generation, training, evaluation, deployment, and back to data generation.\n\nPerforms code reviews and regression tests; triages and fixes issues; at senior levels, leads design and testing efforts.\n\nCollaborates with hardware, systems, test, and AI operations teams so simulation integrates with the rest of the program; at senior levels, engages external partners and industry collaborations to advance simulation technology and shape shared roadmaps.\n\nWrites and reviews technical documentation, design rationale, and knowledge-sharing material for complex software projects.\n\nAt senior levels, articulates technical findings and engineering due diligence - measured capabilities, trade-offs, and honest limits - to leadership, informing roadmap and strategy decisions.\n\nQualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail  or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).\nTo all Staffing and Recruiting Agencies:Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.\nEEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.\nQualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.\nPay rangeand Other Compensation & Benefits :\n$155,400.00 - $233,200.00The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. 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