{"id":105859,"url":"https://alion.io/job/zoox-engineering-manager-onboard-execution-performance","title":"Engineering Manager, Performance 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":"Leadership","role_family":"Leadership","seniority":"lead","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"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":237000,"max":322000,"currency":"USD","period":"year","gross":null,"usd_annual":322000},"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":"Linux","optional":false},{"name":"TensorRT","optional":false},{"name":"XLA","optional":false}],"status":"live","first_seen_at":"2026-04-17T15:58:31Z","employer_posted_date":"2026-04-17","last_verified_at":"2026-10-17T07:27:43Z","board_verified":true,"closed_at":null,"days_open":182,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":182},"description":"Zoox is building the world's most advanced self-driving hardware and software solution. The efficiency demands of such a system require an expert fine tuning of both the compute hardware architecture as well as the algorithms and middleware that runs on it to achieve maximum throughput at the most optimal power levels.\nThe Software Performance team keeps the main onboard computer healthy and performant to ensure that our on-bot autonomy software meets its latency targets while also accommodating its safety goals. We are responsible for the efficient allocation of onboard compute resources (CPU/GPU/Memory/accelerators) ensuring contention is minimized and the autonomy stack executes harmoniously.\nAs the Onboard Execution team leader within the Performance team, you will lead a highly specialized team of software performance engineers in aggressively optimizing on-bot software runtime. This is a high impact role that sits at the intersection of ML Platform, Core, and Autonomy Software - ensuring that efficient compute utilization is a central tenet emphasized across the organization.\nIn this role, you will:\nManage a team of 8-to-10 engineers focused on optimizing the software performance of the on-bot autonomy software stack.\nPartner with Autonomy, ML Platform, Middleware, Sensors, and Hardware Architecture teams to meet strict system response (latency) and compute utilization targets.\nAchieve alignment between cross-functional leaders across the Software organization on compute resource allocations and budgets.\nHire, grow, and develop talent in the realm of performance optimization.\nBuild self-service force-multiplier frameworks & workflows that empower developers to deliver efficient & performant software.\nQualifications\nBS in computer science, computer engineering, or related field\nProficiency with C++ & concurrent (parallel) execution systems\nDeep knowledge and understanding of the Linux operating system and GPU accelerated execution\nProven track record of cross-functional collaboration and leadership alignment\nPrior experience managing teams of five or more full-time software engineers\nBonus Qualifications\nHands-on experience with GPU runtime frameworks (CUDA, TensorRT, or XLA)\nExperience in robotics, aviation, automotive, or high-performance computing space\nHave shipped real-time or near-real-time software autonomy or robotics software in production\nExperiencing debugging and optimizing CPU, GPU kernels, and ML models using tools like NVIDIA Nsight Systems and\nCompute, Perfetto or similar compute observability tools","description_format":"text","description_chars":2565,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"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":"2026-04-17T15:58:31Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 373 · green card filings: 120","filings_12m":373,"filings_prev_12m":311,"green_card_filings_12m":120,"median_offered_wage_usd":181283,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM 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