{"id":460369,"url":"https://alion.io/job/bot-auto-gpu-engineer","title":"GPU Engineer","company":{"id":177011,"name":"Bot Auto","domain":"bot.auto","url":"https://alion.io/company/bot-auto","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":9,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","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":["Houston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":120000,"max_usd":233000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":477},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"NVIDIA Drive","optional":false},{"name":"ONNX","optional":false},{"name":"OpenCL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"TensorRT","optional":false}],"status":"live","first_seen_at":"2026-08-05T23:53:21Z","employer_posted_date":"2026-08-05","last_verified_at":"2026-10-25T01:29:49Z","board_verified":true,"closed_at":null,"days_open":80,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":80},"description":"Company Introduction\nAt Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.\nYou would collaborate with software engineers, AI researchers, and hardware specialists to develop high-performance solutions that meet the stringent requirements of autonomous driving applications. This is an exciting opportunity to work on next-generation transportation technology and make a meaningful impact on the future of mobility.\nKey Responsibilities\n Optimize end-to-end GPU performance for real-time autonomous driving workloads, including sensor processing (e.g., camera, LiDAR) and neural network inference.\nDevelop and optimize parallel computing algorithms and GPU-accelerated components using technologies such as CUDA.\nCollaborate with cross-functional teams to design and improve onboard GPU software architectures that meet the computational requirements of perception, planning, and control modules.\nProfile and analyze bottlenecks across GPU computation, memory access, data movement, synchronization, and CPU-GPU interaction.\nDebug and optimize GPU-based software to improve latency, throughput, resource utilization, and runtime stability on embedded platforms.\nQualifications:\nRequired:\nBachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.\nStrong knowledge of parallel computing principles, GPU architecture, memory hierarchy, and performance optimization techniques.\nExperience profiling GPU applications using tools such as NVIDIA Nsight Systems, Nsight Compute, or equivalent tools.\nExperience deploying or optimizing neural network inference workloads using technologies such as PyTorch, ONNX, and TensorRT.\nExperience with real-time embedded systems and handling large data streams from sensors (camera, LiDAR, radar).\nStrong proficiency in C/C++ and Python.\nPreferred:\n 3+ years of experience in GPU programming and optimization (e.g., CUDA, OpenCL, Vulkan).\nExperience with NVIDIA Jetson Thor, NVIDIA DRIVE Thor, or similar embedded GPU platforms.\nExperience with model quantization, including FP8 and NVFP4.\nExperience managing concurrent GPU workloads and resource isolation using technologies such as NVIDIA Multi-Process Service (MPS), Multi-Instance GPU (MIG), or other related 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