Zoox
From Zoox's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
You will work alongside a team of strong software engineers and act as a force multiplier for our internal customers.
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.
The Opportunity Are you excited to drive our ML Inference Optimization initiatives and make our ML models that enable autonomous driving as fast and efficient as possible? You will get to work with SOTA accelerators, cutting-edge techniques in distributed training, quantization, distillation, and pruning, among other things, working closely with all the Autonomy teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox.
We build and operate the base layer of ML tools, model development, and serving 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 here. If you want to learn more about our ML Infrastructure, here is one of our past talks at re:Invent.
Design, implement, and operate cutting-edge ML Training OR Inference performance optimization techniques to scale our VLM, VLA, and Foundational models and deploy them efficiently in our robotaxi.
4+ years of total experience, including 2+ years of working on large-scale model training or inference platforms.
Experience with training frameworks like PyTorch, leveraging GPUs efficiently for distributed model training.
Experience with GPU-accelerated inference using TensorRT or similar frameworks.
Experience using profiling tools like NVIDIA's Nsight or PyTorch's Profiler for identifying model training and serving bottlenecks.
Proficient in Python or C++.
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Hi [Name], I just applied for the Software Engineer, ML Inference Optimization role at Zoox. My background in [your most relevant experience] lines up well with what the team is looking for. Would you be open to a quick chat, or could you point me to the right person on the team? Thank you!
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