{"id":105814,"url":"https://alion.io/job/zoox-seniorstaff-software-engineer-machine-learning-system-optimization","title":"Senior/Staff Software Engineer - Machine Learning & System Optimization","company":{"id":3626,"name":"Zoox","domain":"zoox.com","url":"https://alion.io/company/zoox","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":72,"open_postings":42,"ghost_share":0,"stale_share":0.905,"repost_share":0,"time_to_fill_p50_days":85,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Foster City, United States","Seattle, United States","Boston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":254000,"max":350000,"currency":"USD","period":"year","gross":null,"usd_annual":350000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"Sensor Fusion","optional":false},{"name":"TensorRT","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"VLM","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-06-30T21:45:57Z","employer_posted_date":"2026-06-30","last_verified_at":"2026-10-17T23:12:52Z","board_verified":true,"closed_at":null,"days_open":109,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":109},"description":"The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence.\nAs a Machine Learning and System Optimization Engineer, you will orchestrate and allocate overall system capacity to various core perception models running on-bot, as well as drive large initiatives that allow for more efficient inference by sharing various parts of the perception stack with one another.\nYou will focus on bringing highly efficient, production-ready large-scale models to our on-vehicle stack. We are looking for experts with hands-on experience compressing, accelerating, and deploying complex models, including LLMs, VLMs, or foundation models, for power- and thermal-constrained vehicle SoCs.\nIn addition, you will optimize ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices.\nIn this role, you will:\nAllocate and distribute system resources (CPU/GPU/interconnect) to various models and inference engines running on the robot.\n\nSpearhead cross-cutting initiatives that allow for better compute utilization through sharing/fusing models and better scheduling strategies.\n\nOptimize large-scale models (Multi-Modal Sensor Fusion models, LLMs, VLMs) using advanced quantization (PTQ, QAT), pruning, mixed-precision inference frameworks, and parameter-efficient fine-tuning (LoRA, QLoRA).\n\nArchitect and implement model conversion and compilation pipelines using TensorRT for edge deployment.\n\nWrite production-level, low-latency, and memory-safe C++ and CUDA code for real-time inference on vehicle systems.\n\nQualifications:\nDeep experience in system and performance optimization in CPU/GPU systems designed for low latency or high throughput.\n\nDeep expertise in working with real-time systems & required constraints such as processing latency, memory utilization, and memory bandwidth pressure.\n\nDeep expertise in model quantization (PTQ, QAT) and mixed-precision inference frameworks (INT8, FP8, FP4, BF16/FP16).\n\nProficiency in low-level programming for AI accelerators, specifically developing and optimizing custom ML OPs and TensorRT Plugins with efficient CUDA kernel implementations.\n\nProduction-level C++ (14/17/20) and Python programming skills, with experience developing concurrent, memory-safe, real-time inference code for edge devices.\n\nBonus Qualifications:\nPrior experience in high-performance robotics applications such as AV/drones/robots.\n\nFamiliarity with SOTA autonomous driving perception algorithms (temporal 3D object detection, BEV, 3D Occupancy Networks) and multi-modal sensor processing (Vision, LiDAR, Radar).\n\nExperience with end-to-end autonomous driving paradigms (VLM/VLA models, Foundation models) and edge deployment technologies (e.g., TensorRT-LLM).","description_format":"text","description_chars":2845,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Autonomous Driving","Smart Mobility","Automotive Manufacturing","Ride Hailing"],"lifecycle":[{"event":"open","at":"2026-06-30T21:45:57Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 373 · 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