{"id":68597,"url":"https://alion.io/job/phantom-seniorstaff-system-software-engineer-adas-vision-platform","title":"Senior/Staff System Software Engineer — ADAS Vision Platform","company":{"id":27037,"name":"Phantom","domain":"phantom.ai","url":"https://alion.io/company/phantom-ai","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"DevOps","role_family":"DevOps","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":156000,"max_usd":302000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":540},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C","optional":false},{"name":"C++","optional":false},{"name":"Computer Vision","optional":false},{"name":"Linux","optional":false},{"name":"ROS","optional":false},{"name":"ROS2","optional":false},{"name":"ZeroMQ","optional":false},{"name":"CUDA Toolkit","optional":true},{"name":"RTOS","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-08-10T21:46:20Z","employer_posted_date":"2026-08-10","last_verified_at":"2026-10-17T03:26:05Z","board_verified":true,"closed_at":null,"days_open":67,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":67},"description":"About Us\nPhantomVision™ is Phantom AI's production computer vision system for ADAS: a multi-camera, real-time perception stack that ships on automotive ECUs running Linux, QNX, and INTEGRITY. Behind the perception algorithms sits the platform that makes them run - the real-time runtime, the sensor and data pipelines, the simulation and test infrastructure, and the interfaces to the vehicle. This role owns that platform.\nYou'll work at the boundary where algorithms meet hardware, where recorded data meets live systems, and where a research idea becomes software that runs deterministically at 30 frames per second in a moving car.\n What you'll do\nOwn the runtime infrastructure of a real-time vision system: task orchestration, inter-process messaging, sensor ingestion, and deterministic data flow under hard latency budgets\nBuild the infrastructure that lets the vision stack run anywhere - on target ECUs, on a developer's desk, and at scale against large volumes of recorded drive data\nDesign and evolve data replay, simulation, and regression-testing systems that turn field recordings into fast, repeatable development loops\nKeep the stack portable and efficient across compute platforms and DNN inference runtimes, from datacenter GPUs to automotive SoCs\nDesign the real-time communication paths that connect the vision system to vehicle platforms and external consumers\nEngineer to automotive safety and security standards (ISO 26262, ISO 21434) without giving up development speed\nBuild and sharpen the team's AI-assisted engineering workflows - coding agents, AI-assisted code review, test generation - and set the standard for how they're used well\nWhat you bring\n6+ years building system software for real-time or embedded environments\nExpert-level C++ (and solid C), including debugging on real hardware under real-time constraints\nDeep grounding in operating systems and computer architecture: scheduling, memory, caches, IPC, and what they cost\nHands-on ROS or ROS2 experience - designing nodes/components, defining messages, working with bag data\nFluency in serialization and middleware (Protocol Buffers, DDS, ZeroMQ, or similar) and network programming over TCP/UDP/Ethernet\nWorking practice with AI-assisted development tools as a normal part of how you build, debug, and test\nBS/MS in Computer Science or equivalent experience\nNice to have\nDepth in an RTOS or safety-grade platform: QNX, RT Linux, or Green Hills INTEGRITY\nExperience across both ROS1 and ROS2 ecosystems, including bag formats (rosbag/rosbag2/MCAP) and DDS configuration\nDNN inference runtimes and accelerators: TensorRT, TIDL, CUDA, quantized inference, and their pre/post-processing pipelines\nCloud-scale data or simulation pipelines: containers, orchestration, batch compute, CI at fleet-data volume\nAutomotive communication protocols: CAN, automotive Ethernet, SOME/IP\nTime spent shipping software in an automotive, robotics, or autonomous-vehicle setting\nBenefit\nMedical, dental, and vision coverage\nOffice snacks & reimbursable meals*\nPaid Time Off \nFSA\n401K\n Equal Opportunity for Diversity & Inclusion\nPhantom AI provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, 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