AI ML Engineer Jobs at Zoox with Visa Sponsorship
AI ML Engineer jobs at Zoox involve working on perception, prediction, and planning systems for autonomous vehicles built from the ground up, systems that have to perform in real-world conditions. Zoox has a consistent record of sponsoring work visas across multiple categories for this function, making it a realistic target if you need sponsorship.
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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 Engineer Jobs at Zoox
Frame your ML work around safety-critical systems
Zoox's hiring panel weighs experience in high-reliability, real-time ML systems heavily. Reframe past projects in terms of system safety, model robustness, and production deployment constraints before you apply, not after you get an interview.
Target roles tied to core autonomy stacks
Perception, motion forecasting, and sensor fusion roles at Zoox see the strongest sponsorship activity. Prioritize these over peripheral data or tooling positions if visa sponsorship is your primary constraint and you have relevant depth.
Verify your OPT STEM extension eligibility early
If you're on F-1 OPT, confirm your degree qualifies for the 24-month STEM extension before accepting an offer. Zoox participates in E-Verify, which is a requirement for STEM OPT authorization, so that part is covered on their end.
Use Migrate Mate to surface open AI ML Engineer roles at Zoox
Zoox posts AI ML Engineer openings across multiple teams and locations. Use Migrate Mate to filter for sponsorship-confirmed roles at Zoox specifically, so you're not guessing which postings will support your visa category.
Ask about H-1B cap timing during the offer stage
If you're not currently in H-1B status, your start date depends on the October 1 cap year. Raise this with the recruiter before signing, so both sides align on a realistic timeline rather than discovering the constraint post-offer.
Request a Labor Condition Application preview before your start date
The DOL-certified LCA locks in your worksite and wage level. If Zoox plans to file for a specific location, confirm that detail matches your expected team and office before the LCA is submitted to avoid amendments later.
Frequently Asked Questions
Does Zoox sponsor H-1B visas for AI ML Engineers?
Yes, Zoox sponsors H-1B visas for AI ML Engineers. The company also sponsors H-1B1 visa, E-3 visa, TN visa, and Green Card pathways including EB-2 and EB-3 categories. AI ML Engineer roles fall squarely within specialty occupation requirements given the degree-level qualifications involved, which means H-1B visa petitions for this function are well-supported by the role's technical nature.
How do I apply for AI ML Engineer jobs at Zoox?
Applications go through Zoox's careers portal. For visa-sponsored roles specifically, Migrate Mate filters Zoox's AI ML Engineer openings by sponsorship type so you can identify which positions are actively supporting your visa category. Tailor your application toward Zoox's autonomous vehicle stack, with emphasis on production ML systems, model deployment, and any robotics or sensor work you've done.
Which visa types does Zoox commonly use for AI ML Engineers?
Zoox supports a broad range of visa categories for this role, including H-1B, H-1B1 visa for Singaporean and Chilean nationals, E-3 for Australian citizens, and TN for Canadian and Mexican nationals. F-1 OPT and CPT are used for early-career candidates, and the company also sponsors immigrant visa categories including EB-2 and EB-3 for longer-term permanent residence pathways.
What qualifications does Zoox expect for AI ML Engineer roles?
Most AI ML Engineer roles at Zoox require a bachelor's degree at minimum in computer science, electrical engineering, or a closely related field, with graduate degrees preferred for research-oriented positions. Practical depth in areas like deep learning, computer vision, or probabilistic modeling matters more than credentials alone, particularly for roles tied to Zoox's perception or prediction systems where production deployment experience is closely evaluated.
How long does the visa sponsorship process take for a role at Zoox?
Timeline depends heavily on your current status. E-3 and TN visas can be processed within weeks at a consulate or port of entry. H-1B cap-subject petitions follow a fixed government schedule, with a start date no earlier than October 1 following the April lottery. USCIS premium processing reduces adjudication to 15 business days once a petition is filed, which many employers use to reduce uncertainty during onboarding.