AI Engineer Jobs at Zoox with Visa Sponsorship
AI Engineer jobs at Zoox involve working at the intersection of perception, prediction, and real-world robotics deployment, as the company builds fully autonomous vehicles from the ground up. Zoox has a consistent track record of sponsoring international talent across multiple visa categories for engineering functions like this one.
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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 Engineer Jobs at Zoox
Align your portfolio to Zoox's stack
Zoox's AI work spans perception systems, sensor fusion, and motion planning for autonomous vehicles. Frame your projects around production-scale ML pipelines or real-world robotics datasets, not just academic benchmarks. Hiring teams here evaluate applied autonomy experience directly.
Confirm your visa type before applying
Zoox sponsors several nonimmigrant categories including H-1B, E-3, TN, and F-1 OPT. Know which category fits your citizenship and status before your first recruiter screen so you can answer sponsorship questions with confidence instead of uncertainty.
Target roles that map to specialty occupation criteria
USCIS requires H-1B positions to meet specialty occupation standards. AI Engineer roles at Zoox typically involve a direct degree requirement in computer science, electrical engineering, or a closely related field. Highlight your specific degree field in your application materials.
Negotiate your start date around H-1B cap timelines
If you need a new H-1B and aren't already cap-exempt, USCIS only allows October 1 start dates for cap-subject petitions. Raise this with your Zoox recruiter early so the offer letter and onboarding timeline can account for it without last-minute complications.
Use Migrate Mate to surface Zoox AI Engineer openings
Zoox posts AI Engineer roles across perception, prediction, and planning subteams at different times throughout the year. Use Migrate Mate to filter and track openings at Zoox that explicitly support visa sponsorship, so you're not manually sifting through general job boards.
Prepare for a technical loop that mirrors production systems
Zoox's interview process for AI Engineers typically includes system design rounds focused on autonomous vehicle architectures. Practice designing end-to-end inference pipelines and be ready to discuss latency, safety constraints, and hardware trade-offs at the level of a deployed product.
Frequently Asked Questions
Does Zoox sponsor H-1B visas for AI Engineers?
Yes, Zoox sponsors H-1B visas for AI Engineer roles. The company has an established history of filing H-1B petitions for engineering positions, including those in AI, perception, and autonomy. If you're subject to the H-1B cap, your offer and start date will need to align with USCIS's annual lottery and October 1 activation window. Zoox's recruiting team typically coordinates on this during the offer stage.
Which visa types does Zoox commonly use for AI Engineer roles?
Zoox sponsors several categories for AI Engineers, including H-1B, H-1B1 visa for Chilean and Singaporean nationals, E-3 visa for Australian nationals, TN visa for Canadian and Mexican professionals under USMCA, and F-1 OPT and CPT for students. The right category depends on your citizenship and current immigration status. Zoox also supports Green Card sponsorship through EB-2 and EB-3 pathways for longer-term employees.
What qualifications does Zoox expect for AI Engineer positions?
Zoox typically looks for a bachelor's or master's degree in computer science, electrical engineering, robotics, or a directly related field. For H-1B purposes, USCIS classifies these as specialty occupations requiring a specific degree. Hands-on experience with deep learning frameworks, sensor data processing, or large-scale training infrastructure is consistently valued. Research publications or contributions to open-source autonomy projects can strengthen your candidacy.
How do I apply for AI Engineer jobs at Zoox?
You can browse and apply for AI Engineer positions at Zoox through Migrate Mate, which filters roles by visa sponsorship type so you can confirm eligibility before applying. Zoox's technical hiring process includes a recruiter screen, coding assessments, and multiple rounds covering ML system design and domain-specific problem-solving. Tailoring your resume to reflect autonomous systems experience directly improves your chances of passing initial screening.
How do I plan my timeline if I'm on F-1 OPT targeting a full-time AI Engineer role at Zoox?
If you're on F-1 OPT with STEM extension eligibility, you have up to 36 months of work authorization, which provides a meaningful runway. Zoox can file an H-1B registration on your behalf during the annual cap lottery period in March. If selected, your H-1B activates October 1. USCIS requires employers to be registered before the lottery opens, so you'll want an offer finalized by late February at the latest.