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HomeCompaniesZooxMachine Learning Engineer - MLA Perception Offline Driving Intelligence

Machine Learning Engineer - MLA Perception Offline Driving Intelligence

Zoox · Foster City, CA · Hybrid · Active · $179,000–$245,000 / year · Lever

Job facts

FieldValue
CompanyZoox
TitleMachine Learning Engineer - MLA Perception Offline Driving Intelligence
Normalized title-
Department / teamSoftware / Autonomy Software
LocationFoster City, CA, United States
Work modelHybrid / Hybrid
Employment typeFull Time
Salary$179,000–$245,000 / year
Statusactive
ATS providerLever
Posted / first seen2025-12-19 / 2026-05-29
Changed / last seen2026-06-06 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Zoox.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through Lever.Open
Provider filtered searchThe same provider as a filtered job collection.Open
City jobsActive postings in Foster City.Open
Department jobsActive postings in Software.Open
Work model jobsActive Hybrid postings.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyZoox
Source45f1a12e-419b-4b96-93be-f479c9356a1b
ATS providerLever

Description

The Offline Driving Intelligence (ODIN) team at Zoox is leveraging the latest in AI to craft algorithms that understand the world. We leverage large models first offline and we devise a path of impact into our self-driving robot, enabling safe and efficient navigation in complex environments. As an engineer in the ODIN team, you will develop advanced multimodal large language models that enhance environmental understanding. You'll develop and fine-tune these models for off-vehicle analysis while working with the onboard team to deliver impact in our robotaxi platform, ensuring they can efficiently identify hazards and interpret driving restrictions with minimal latency. Working alongside world-class engineers and researchers, you'll leverage premium sensor data and cutting-edge infrastructure to validate your algorithms in real-world conditions, directly impacting productivity, safety and the capability of Zoox's autonomous system. About Zoox Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team. Follow us on LinkedIn Accommodations If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter. A Final Note: You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. In this role, You will... Develop multimodal large language models that enhance our robotaxis' understanding of complex urban environments Implement model architectures and sophisticated training techniques Build large high quality datasets leveraging all the inputs from our sensor stack and the overall large scale data we have at Zoox. Drive end-to-end ML solutions from research to production, utilizing Zoox's extensive data pipelines and infrastructure to improve autonomous driving capabilities. Collaborate with perception, planning, safety, and systems teams to integrate your models into the vehicle's decision-making pipeline. Validate and optimize your solutions using real-world driving scenarios, directly contributing to the safety and reliability of Zoox's autonomous system Qualifications MS or PhD in Computer Science, Machine Learning, or related technical field Demonstrated experience training and deploying large language models (LLMs) Experience building and maintaining ML training pipelines, including data preprocessing, model training, and evaluation Proficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projects Experience training models with large scale data Bonus Qualifications Publications in top-tier conferences (CVPR, ICCV, RSS, ICRA) Experience with autonomous robotics systems

Full job record

Job ID2f5a1384935db527d1654e13c5ff77d0fe876f31
Org ID518be277-8ec5-4735-b0ad-193a2bc397c7
Source ID45f1a12e-419b-4b96-93be-f479c9356a1b
Board ID45f1a12e-419b-4b96-93be-f479c9356a1b
Providerlever
Provider Job Key809b1301-9654-4673-8f2d-4bc68bcead7f
TitleMachine Learning Engineer - MLA Perception Offline Driving Intelligence
Normalized Title
Statusactive
Activeyes
Location TextFoster City, CA
DepartmentSoftware
TeamAutonomy Software
Employment TypeFull-time
Workplace Typehybrid
Remote Policyhybrid
CountryUnited States
RegionCA
CityFoster City
Salary RawUSD 179000-245000 per-year-salary
Salary Min179,000
Salary Max245,000
Salary CurrencyUSD
Salary Periodyear
Source URLhttps://jobs.lever.co/zoox/809b1301-9654-4673-8f2d-4bc68bcead7f
Apply URLhttps://jobs.lever.co/zoox/809b1301-9654-4673-8f2d-4bc68bcead7f/apply
First Seen At2026-05-29 06:58:06Z
Last Seen At2026-06-06 20:04:34Z
Last Checked At2026-06-06 20:04:34Z
Last Changed At2026-06-06 07:55:46Z
Inactive At
Source Posted At2025-12-19 19:26:02Z
Source Updated At
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=lever/board=zoox/date=2026-06-06/2026-06-06T20-04-33-960Z-dbc899b7b70bd68deef4fecc07510b625903b1c9c1b990b1843279904e7d9bc6.json
Event Fields
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Parsed Structured
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Extensions
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Native Structured
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