AI ML Engineering Jobs at Zoox with Visa Sponsorship
AI ML Engineering jobs at Zoox involve building autonomous vehicles from the ground up, working on perception, prediction, and planning systems that require deep research expertise. Zoox has an established track record of sponsoring work visas across multiple categories for this function.
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Get Access To All JobsINTRODUCTION
The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence. As a Model Optimization & Deployment Engineer, you 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 in compressing, accelerating, and deploying complex models (LLMs, VLMs, or FMs) for power- and thermal-constrained vehicle SOCs. You will optimize the ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices.
ROLE AND RESPONSIBILITIES
- Optimize large-scale models (LLMs, VLMs) using advanced quantization (PTQ, QAT), mixed-precision inference workflows, and parameter-efficient fine-tuning (LoRA, QLoRA).
- Architect and implement model conversion and compilation pipelines using TensorRT and TensorRT-LLM for edge deployment.
- Perform rigorous parity checking, accuracy recovery, and latency benchmarking between PyTorch frameworks and compiled edge binaries.
- Write and optimize custom CUDA kernels and TensorRT Plugins to maximize memory bandwidth and minimize latency on AI accelerators.
- Write production-level, highly concurrent, and memory-safe C++ and Python code for real-time inference on vehicle SOCs.
BASIC QUALIFICATIONS
- Deep expertise in model quantization (PTQ, QAT) and mixed-precision inference workflows (INT8, FP8, INT4, BF16/FP16).
- Proven experience optimizing large-scale models (LLMs, VLMs, or VLAs) utilizing KV-cache optimization (e.g., PagedAttention), Speculative Decoding, and Efficient Attention mechanisms (FlashAttention, Linear Attention).
- Extensive experience with model conversion/compilation pipelines (TensorRT, TensorRT-LLM) and performing rigorous parity/latency benchmarking.
- Proficiency in low-level programming for AI accelerators, specifically writing and optimizing custom CUDA kernels and TensorRT Plugins.
- Production-level C++ (14/17/20) and Python programming skills, with experience writing concurrent, memory-safe, real-time inference code for edge devices.
PREFERRED QUALIFICATIONS
- Experience with distributed training pipelines and model/tensor parallelism (PyTorch Distributed, Ray, DeepSpeed, Megatron-LM) and runtime efficiency optimization for GPU clusters.
- Familiarity with autonomous driving perception stacks (temporal 3D object detection, BEV, 3D Occupancy Networks) and processing multi-modal sensor streams (Vision, LiDAR, Radar).
- Understanding of end-to-end autonomous driving paradigms (VLA models, closed-loop simulation validation).
COMPENSATION
- Base Salary Range: $242,000 - $290,000 a year
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position. Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
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.
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.
INTRODUCTION
The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence. As a Model Optimization & Deployment Engineer, you 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 in compressing, accelerating, and deploying complex models (LLMs, VLMs, or FMs) for power- and thermal-constrained vehicle SOCs. You will optimize the ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices.
ROLE AND RESPONSIBILITIES
- Optimize large-scale models (LLMs, VLMs) using advanced quantization (PTQ, QAT), mixed-precision inference workflows, and parameter-efficient fine-tuning (LoRA, QLoRA).
- Architect and implement model conversion and compilation pipelines using TensorRT and TensorRT-LLM for edge deployment.
- Perform rigorous parity checking, accuracy recovery, and latency benchmarking between PyTorch frameworks and compiled edge binaries.
- Write and optimize custom CUDA kernels and TensorRT Plugins to maximize memory bandwidth and minimize latency on AI accelerators.
- Write production-level, highly concurrent, and memory-safe C++ and Python code for real-time inference on vehicle SOCs.
BASIC QUALIFICATIONS
- Deep expertise in model quantization (PTQ, QAT) and mixed-precision inference workflows (INT8, FP8, INT4, BF16/FP16).
- Proven experience optimizing large-scale models (LLMs, VLMs, or VLAs) utilizing KV-cache optimization (e.g., PagedAttention), Speculative Decoding, and Efficient Attention mechanisms (FlashAttention, Linear Attention).
- Extensive experience with model conversion/compilation pipelines (TensorRT, TensorRT-LLM) and performing rigorous parity/latency benchmarking.
- Proficiency in low-level programming for AI accelerators, specifically writing and optimizing custom CUDA kernels and TensorRT Plugins.
- Production-level C++ (14/17/20) and Python programming skills, with experience writing concurrent, memory-safe, real-time inference code for edge devices.
PREFERRED QUALIFICATIONS
- Experience with distributed training pipelines and model/tensor parallelism (PyTorch Distributed, Ray, DeepSpeed, Megatron-LM) and runtime efficiency optimization for GPU clusters.
- Familiarity with autonomous driving perception stacks (temporal 3D object detection, BEV, 3D Occupancy Networks) and processing multi-modal sensor streams (Vision, LiDAR, Radar).
- Understanding of end-to-end autonomous driving paradigms (VLA models, closed-loop simulation validation).
COMPENSATION
- Base Salary Range: $242,000 - $290,000 a year
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position. Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
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.
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.
Tips for Finding AI ML Engineering Jobs at Zoox
Frame your ML research for autonomy
Zoox evaluates AI ML candidates on perception, sensor fusion, and motion planning work specifically. Reframe prior experience around safety-critical or real-time inference systems rather than general model training to align with what their engineering teams prioritize.
Target roles matching your visa timeline
If your F-1 OPT expires within six months of applying, flag that directly with Zoox's recruiting team early. Autonomous vehicle companies have longer onboarding cycles, so misaligned timelines can complicate an otherwise strong candidacy.
Understand which petitions Zoox files first
For H-1B candidates already in the U.S., Zoox typically handles change of status through USCIS rather than consular processing. Confirm this with your recruiter so your I-129 filing timeline and start date don't conflict with your current status expiry.
Prepare specialty occupation documentation early
AI ML Engineering at Zoox falls squarely within USCIS specialty occupation standards, but your offer letter and degree transcript need to show direct field alignment. A computer science or electrical engineering degree maps cleanly; a tangential field needs a credentials evaluation from a NACES-recognized evaluator before the petition.
Use Migrate Mate to identify open AI ML roles
Zoox's AI ML Engineering openings are spread across research and applied teams and don't always surface together on general job boards. Use Migrate Mate to browse currently open, visa-sponsoring positions at Zoox filtered by role type and visa category.
Account for PERM timelines if targeting a Green Card
Zoox sponsors EB-2 and EB-3 Green Cards for AI ML Engineering staff, but PERM labor certification through DOL can take 18 months or longer at current processing speeds. Factor this into your multi-year visa planning before your first H-1B term ends.
Frequently Asked Questions
Does Zoox sponsor H-1B visas for AI ML Engineers?
Yes, Zoox sponsors H-1B visas for AI ML Engineering roles. The company files petitions through the standard USCIS cap process for new entrants and also supports cap-exempt transfers for candidates already holding H-1B status with another employer. If you're an F-1 OPT holder, Zoox can bridge you through the lottery cycle, though you should align your OPT expiry and any STEM extension with the recruiter during the offer stage.
Which visa types does Zoox commonly sponsor for AI ML Engineering roles?
Zoox sponsors a broad range of visa categories for AI ML Engineering, including H-1B, H-1B1 visa for Chilean and Singaporean nationals, E-3 visa for Australian citizens, TN visa for Canadian and Mexican professionals, and F-1 OPT and CPT for students. For longer-term pathways, Zoox also supports EB-2 and EB-3 Green Card sponsorship, which is relevant for candidates thinking beyond initial work authorization.
How do I apply for AI ML Engineering jobs at Zoox?
Applications go through Zoox's careers portal, where AI ML Engineering roles are listed across research, applied, and systems teams. The process typically involves an initial recruiter screen, technical interviews covering ML fundamentals and domain-specific problems like perception or planning, and a final system design round. To find currently open AI ML Engineering positions at Zoox that offer visa sponsorship, browse Migrate Mate, which filters roles specifically by visa type and employer.
What qualifications does Zoox expect for AI ML Engineering candidates?
Zoox expects a graduate degree in computer science, electrical engineering, robotics, or a closely related field for most AI ML Engineering roles, with strong preference for Ph.D. holders in research-oriented positions. Practical experience with deep learning frameworks, sensor data pipelines, or real-time inference systems is weighted heavily. Published research or prior work in autonomous systems, perception, or prediction models is a meaningful differentiator during the technical review process.
How do I think about visa filing timelines when accepting an offer from Zoox?
If you need an H-1B cap filing, USCIS registration opens in March for an October 1 start date, so offers accepted outside that window require bridging through OPT, CPT, or another status. E-3 and TN visas process faster and aren't subject to lottery constraints, which gives Australian, Canadian, and Mexican candidates more flexibility on start dates. Confirm your specific category and status expiry with Zoox's immigration counsel before signing your offer to avoid gaps in work authorization.