{"id":1963791,"url":"https://alion.io/job/nvidia-deep-learning-senior-engineer-end-to-end-autonomous-driving-2","title":"Deep Learning Senior Engineer, End-To-End Autonomous Driving","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":775,"ghost_share":0.025,"stale_share":0.735,"repost_share":0.043,"time_to_fill_p50_days":35,"computed_at":"2026-10-22T06:10:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States","United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":184000,"max":287500,"currency":"USD","period":"year","gross":null,"usd_annual":287500},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Motion Planning","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"VLM","optional":false}],"status":"live","first_seen_at":"2026-10-06T16:39:42Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-23T01:23:48Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"At NVIDIA, we are seeking exceptional engineers to join our autonomous driving team to design, implement, and deploy cutting-edge end-to-end autonomous driving systems, running on NVIDIA chips in mass-production vehicles. Our strategy has evolved from AI 1.0 - building a driver from scratch - to AI 2.0 - teaching an intelligent agent to drive. This next phase leverages LLMs, VLMs, and VLAs to bring unprecedented reasoning, planning capabilities, and interactivity with the driving system to autonomous vehicles and general robotics. Let’s build the future of autonomy-together!\nWhat You’ll Be Doing:\nDesign and train innovative large-scale models-including generative, imitation, and reinforcement learning-to improve the planning and reasoning capabilities of our driving systems.\nBuild, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications.\nExplore novel data generation and collection strategies to improve diversity and quality of training datasets.\nCollaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met.\nIntegrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.\nWhat We Need to See:\nHands-on experience building LLMs, VLMs, or VLAs from scratch or a proven track record as a top-tier coder passionate about autonomous systems.\nDeep understanding of modern deep learning architectures and optimization techniques.\nProven record of deploying production-grade ML models for self-driving, robotics, or related fields at scale.\nStrong programming skills in Python and proficiency with major deep learning frameworks.\nFamiliarity with C++ for model deployment and integration in safety-critical systems.\nPhD with 4+ years, MS (or equivalent experience) with 6+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.\nWays to Stand Out from the Crowd:\nExperience with LLM/VLM/VLA systems deployable to autonomous vehicles or general robotics.\nPublications, open-source contributions, or competition wins related to LLM/VLM/VLA systems.\nDeep understanding of behavior and motion planning in real-world AV applications.\nExperience building and training large-scale datasets and models.\nProven ability to optimize algorithms for real-time performance in resource-constrained environments and strong track record of taking projects from concept to production deployment.\nYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.\nYou will also be eligible for equity and benefits.\nApplications for this job will be accepted at least until October 10, 2026.This posting is for an existing vacancy.\nNVIDIA uses AI tools in its recruiting processes.\nNVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. 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