Zoox

<div><span>Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We are still in the early stages of deploying our robotaxis on public roads, and it is a great time to join Zoox and have a significant impact in executing this mission. The ML Platform team at Zoox plays a crucial role in enabling innovations in large-scale Foundation models, VLMs, and VLAs to make autonomous driving as seamless as possible.</span></div>
<div> </div>
<div><strong><span>The Opportunity</span></strong></div>
<div><span>Are you excited to drive our ML Training Optimization initiatives and make the models that power autonomous driving trains faster, cheaper, and at greater scale? You will work with SOTA accelerators and cutting-edge techniques in distributed training, parallelism strategies, mixed-precision training and RL, especially for VLA and VLM models that are multi-modal — partnering closely with all the Autonomy teams within Zoox (Perception, Prediction, Planner, Simulation, Collision Avoidance) and pushing the boundaries of how ML training is practiced at Zoox.</span></div>
<div> </div>
<div><span>We build and operate the base layer of ML tools, model development, and training systems that our applied research teams use for in- and off-vehicle ML use cases. You will work alongside a team of strong software engineers and act as a force multiplier for our internal customers. This team has many growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our stack behind autonomous driving, please look</span><a href="https://zoox.com/autonomy/" rel="noopener noreferrer ugc" target="_blank"><span> </span><span>here</span></a><span>. If you want to learn more about our ML Infrastructure, here is one of our</span><a href="https://www.youtube.com/watch?v=j-LpPv9vBvk" rel="noopener noreferrer ugc" target="_blank"><span> </span><span>past talks</span></a><span> at re:Invent.</span></div>
<h2>In this role, you will:</h2><div>

<li>
<p><span>Design, implement, and operate cutting-edge ML training performance optimization techniques — distributed training (data/tensor/pipeline parallelism), mixed-precision, gradient checkpointing, and GPU kernels — to scale our VLM, VLA, and Foundational models training efficiency.</span></p>
</li>
<li>
<p><span>Work on RL framework and RL performance optimization for driving foundation model </span></p>
</li>
<li>
<p><span>Profile and debug training throughput bottlenecks across the compute, compiler, framework, and cluster-networking stack.</span></p>
</li>
<li>
<p><span>Collaborate closely with ML researchers, data engineers, and hardware engineers to define requirements and align on architectural decisions for large-scale training infrastructure.</span></p>
</li>
<li>
<p><span>Implement custom kernels and build benchmarks to track and validate training-performance optimizations across the stack.</span></p>
</li>

</div>
<h2>Qualifications</h2><div>
<p><span>Note: You do not have to meet all the requirements below to be considered for this position:</span></p>
<ul>
<li>
<p><span>4+ years of total experience, including 2+ years working on large-scale model training platforms.</span></p>
</li>
<li>
<p><span>Experience with distributed training frameworks (Megatron-LM, TorchTitan), efficiently utilizing GPU/accelerator clusters at scale.</span></p>
</li>
<li>
<p><span>Experience with RL framework such as Verl/MILES and RL algorithms(GRPO, PPO)</span></p>
</li>
<li>
<p><span>Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training.</span></p>
</li>
<li>
<p><span>Experience using profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to identify and resolve training bottlenecks.</span></p>
</li>
<li>
<p><span>Proficient in Python or C++.</span></p>
</li>
<li>
<p><span>Experience with ML compilers, performance tooling, or hardware/software co-design (e.g., GPU/TPU/accelerator architectures).</span></p>
</li>

</ul></div>

Software Engineer, ML Training Optimization

<p><span>Zoox is on a mission to reimagine transportation and build ground-up autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We’re still early in deploying on public roads, so this is a great time to join and have a significant impact. The ML Platform team enables innovation in large-scale Foundation models, VLMs, and VLAs to make autonomous driving as seamless as possible.</span></p>
<p><strong><span>The Opportunity</span></strong><strong><span><br /></span></strong><span>Are you excited to own ML training optimization at Zoox and make the models that power autonomous driving train faster, cheaper, and at greater scale? You’ll set technical direction for distributed training, parallelism strategies, mixed-precision training, and RL on SOTA accelerators, especially for multi-modal VLA and VLM models. You’ll partner with all Autonomy teams (Perception, Prediction, Planner, Simulation, Collision Avoidance) and act as a force multiplier for our internal customers, while mentoring engineers and shaping the team’s roadmap as we expand into new ML domains. If you want to learn more about our stack behind autonomous driving, please look</span><a href="https://zoox.com/autonomy/" rel="noopener noreferrer ugc" target="_blank"><span> </span><span>here</span></a><span>. If you want to learn more about our ML Infrastructure, here is one of our</span><a href="https://www.youtube.com/watch?v=j-LpPv9vBvk" rel="noopener noreferrer ugc" target="_blank"><span> </span><span>past talks</span></a><span> at re:Invent.</span></p>
<h2>In this role, you will:</h2><div>
<ul>
<li><span>Lead the design, implementation, and operation of training performance optimizations (data/tensor/pipeline parallelism, mixed precision, gradient checkpointing, GPU kernels) to scale VLM, VLA, and Foundation model training.</span></li>
<li><span>Own the technical strategy and multi-quarter roadmap for RL framework and RL performance optimization for driving foundation models.</span></li>
<li><span>Drive profiling and root-cause analysis of throughput bottlenecks across the compute, compiler, framework, and cluster-networking stack.</span></li>
<li><span>Partner with ML researchers, data engineers, and hardware engineers to define requirements and make architectural decisions for large-scale training infrastructure.</span></li>
<li><span>Build custom kernels and benchmarks that track and validate performance gains across the stack.</span></li>
<li><span>Mentor engineers and raise the team’s technical bar through design reviews and best practices.</span></li>

</ul></div>
<h2>Qualifications</h2><div><span>Note: </span><strong><span>You do not have to meet all the requirements</span></strong><span> below to be considered for this position:</span>
<ul>
<li><span>7+ years of total experience</span><span>, including 4+ years on large-scale model training platforms, with a record of leading projects end to end.</span></li>
<li><span>Deep experience with distributed training frameworks (Megatron-LM, TorchTitan) and efficiently utilizing GPU/accelerator clusters at scale.</span></li>
<li><span>Experience with RL frameworks (Verl/MILES) and algorithms (GRPO, PPO).</span></li>
<li><span>Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training.</span></li>
<li><span>Strong command of profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to find and resolve training bottlenecks.</span></li>
<li><span>Proficiency in Python or C++.</span></li>
<li><span>Experience with ML compilers, performance tooling, or hardware/software co-design (GPU/TPU/accelerator architectures).</span></li>
<li><span>Ability to influence across teams and communicate technical tradeoffs to both researchers and engineers.</span></li>

</ul></div>

Senior/Staff Software Engineer, ML Training Optimization

<div><strong>About Zoox</strong><br />Zoox is an autonomous ride-hailing company building the world's first purpose-built robotaxi — fully electric, bidirectional, with no steering wheel or driver's seat. Backed by Amazon and founded to make transportation safer, cleaner, and more accessible, Zoox designs its vehicles entirely around the rider. We're currently operating in Las Vegas and San Francisco, with Austin and Miami on the horizon, and testing underway across seven U.S. markets.</div>
<div><br /><strong>About Our Part-Time Student Worker Program</strong><br />Zoox's part-time student worker program puts you at the center of one of the most ambitious challenges in transportation. You'll contribute to real projects, work alongside engineers and researchers pushing the boundaries of autonomous technology, and gain experience that goes well beyond the classroom. We're looking for students who bring strong academic foundations, curiosity that doesn't stop at coursework, and a drive to be part of something that matters.</div>
<div> </div>
<div><strong>Role Overview</strong></div>
<div>This role requires supporting the end-to-end validation pipeline for AI tools: maintaining test datasets, running benchmarks, and measuring agent accuracy across routing decisions, classification labels, and structured output fields.</div>
<h2>Responsibilities</h2><ul><li>Run and maintain the benchmark pipeline, analyzing results to identify routing errors and regressions across agent variants</li>
<li>Build and expand ground truth datasets used to evaluate agent outputs against known-correct answers</li>
<li>Identify and address gaps in benchmark validation and support building a more comprehensive evaluation infrastructure to improve validation prior to release</li>
<li>Develop new evaluation dimensions such as label accuracy and structured output correctness beyond the existing team classification benchmarks</li>
<li>Investigate failure modes in agent outputs and work with engineers to surface actionable improvements</li>
<li>Write scripts and tooling to automate data collection, result parsing, and metric reporting</li>
<li>Document findings, track benchmark trends over time, and present results to the team</li></ul>
<h2>Program Requirements</h2><ul><li>Currently enrolled in a B.S. or M.S. in Computer Science, Data Science, Engineering or a related field</li>
<li>Available to commit to a minimum three-month assignment</li>
<li>Able to commit to a minimum of 20 hours per week</li>
<li>Able to work on-site at one of our office locations</li>
<li>Must adhere with Zoox confidentiality requirements, including refraining from using or sharing proprietary company information outside of Zoox, such as in academic research, theses, publications, or presentations</li></ul>
<h2>Qualifications</h2><ul><li>Familiar with Cursor or Claude</li>
<li>Familiar with Python</li>
<li>Familiar with evaluation concepts: precision, recall, F1 score, and confusion matrices</li>
<li>Comfortable working with structured data (CSV, JSON)</li>
<li>Experience modifying or writing reproducible analysis scripts</li></ul>
<h2>Bonus Qualifications</h2><ul><li>Prior exposure to LLM-based systems, prompt engineering, or AI agent evaluation</li>
<li>Experience with Jira or Slack (e.g. ticketing systems, messaging apps)</li></ul>

Contract Student Worker – AI Validation and Benchmarking Engineer (40hrs / week Full-Time)

<div>
<p>The Zoox 3D Sensor Simulation team’s work is at the core of creating in-the-loop simulation and high fidelity synthetic data for training and testing autonomous vehicle AI. We are looking for a machine learning professional with experience applying ML to sensor data, such as LiDAR, radar, and cameras.</p>
<p>This role lies at the intersection of machine learning and 3D simulation at Zoox, ranging from learned sensor models to neural 3D reconstruction. By improving the realism of simulated sensors (lidar, radar, and cameras), you’ll enable the Zoox robotics autonomy stack to learn, improve, and enhance the mobility of humans.</p>
</div>
<h2>In this role, you will:</h2><ul><li>Design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data in autonomous vehicle (AV) simulation pipelines.</li>
<li>Collaborate with Synthetic Data and HIL Sim teams to integrate 3DGS and NeRF-based 3D reconstruction with simulation of all sensor modalities.</li>
<li>Build and curate training datasets from real and simulated sensor data, including GenAI-based data augmentation.</li>
<li>Partner with Sim V&amp;V and Perception engineers to define sensor realism metrics and close the sim-to-real gaps with the greatest impact on autonomy.</li>
<li>Mentor engineers and interns, and establish best practices for training and deploying ML on Zoox sensor data.</li></ul>
<h2>Qualifications:</h2><div>
<ul>
<li><span>2+ years of prior experience as a Technical Lead</span></li>
<li>
<p><span>5+ years of experience developing ML models in Python with PyTorch or TensorFlow</span></p>
</li>
<li>
<p><span>Industry experience applying ML to sensor data (lidar, radar, and/or cameras)</span></p>
</li>
<li>
<p><span>Solid understanding of customizing, tuning, and validating ML training pipelines</span></p>
</li>

</ul></div>
<h2>Bonus Qualifications:</h2><ul><li>
<p>Experience with generative techniques such as diffusion, flow matching, or VAEs</p>
</li>
<li>
<p>Experience with 3D reconstruction (3DGS, NeRFs) or differentiable rendering</p>
</li>
<li>
<p>Prior experience developing autonomy in AVs or other robotics and computer vision </p>
</li></ul>

Senior / Staff Machine Learning Engineer - 3D Sensor Simulation

<div>
<p>The Zoox 3D Sensor Simulation team’s work is at the core of creating in-the-loop simulation and high fidelity synthetic data for training and testing autonomous vehicle AI. We are looking for a machine learning professional with experience applying ML to sensor data, such as LiDAR, radar, and cameras.</p>
<p>This role lies at the intersection of machine learning and 3D simulation at Zoox, ranging from learned sensor models to neural 3D reconstruction. By improving the realism of simulated sensors (lidar, radar, and cameras), you’ll enable the Zoox robotics autonomy stack to learn, improve, and enhance the mobility of humans.</p>
</div>
<h2>In this role, you will:</h2><div>
<ul>
<li>
<p><span>Design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data in autonomous vehicle (AV) simulation pipelines.</span></p>
</li>
<li>
<p><span>Collaborate with Synthetic Data and HIL Sim teams to integrate 3DGS and NeRF-based 3D reconstruction with simulation of all sensor modalities.</span></p>
</li>
<li>
<p><span>Build and curate training datasets from real and simulated sensor data, including GenAI-based data augmentation.</span></p>
</li>
<li>
<p><span>Partner with Sim V&amp;V and Perception engineers to define sensor realism metrics and close the sim-to-real gaps with the greatest impact on autonomy.</span></p>
</li>
<li>
<p><span>Mentor engineers and interns, and establish best practices for training and deploying ML on Zoox sensor data.</span></p>
</li>

</ul></div>
<h2>Qualifications:</h2><ul><li><span>3+ years of experience developing ML models in Python with PyTorch or TensorFlow</span></li>
<li><span>Industry experience applying ML to sensor data (lidar, radar, and/or cameras)</span></li>
<li><span>Solid understanding of customizing, tuning, and validating ML training pipelines</span></li></ul>
<h2>Bonus Qualifications:</h2><ul><li>Experience with generative techniques such as diffusion, flow matching, or VAEs</li>
<li>Experience with 3D reconstruction (3DGS, NeRFs) or differentiable rendering</li>
<li>Prior experience developing autonomy in AVs or other robotics and computer vision </li></ul>

Zoox

Open jobs at Zoox

Software Engineer, ML Training Optimization

Senior/Staff Software Engineer, ML Training Optimization

Contract Student Worker – AI Validation and Benchmarking Engineer (40hrs / week Full-Time)

Senior / Staff Machine Learning Engineer - 3D Sensor Simulation

Machine Learning Engineer - 3D Sensor Simulation

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