Machine Learning Engineer

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 ML and CV to make autonomous driving as seamless as possible. The Opportunity Would you like to be part of the ML Training and Inference Infrastructure team that enables autonomous driving and several other ML use cases at Zoox? You will get to work across all ML teams within Zoox - Perception, Prediction, Planner, Simulation, Data Science, Collision Avoidance, etc. and our Advanced Hardware Engineering group 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, deep learning frameworks, and inference libraries used by our applied research teams for in- and off-vehicle ML use cases. You will play a crucial role in reducing the time it takes from ideation to productionization of cutting-edge AI innovation. This team has a lot of 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. In this role, you will: Build the Zoox Training framework leveraged by all ML teams within Zoox. This framework needs to be highly scalable, reliable, and efficient. Build the Off-vehicle inference service powering our Foundational models and the models that improve our rider experiences. Lead the design, implementation, and operation of a robust and efficient ML platform to enable the training, validation, serving, and monitoring of ML models. Collaborate closely with cross-functional teams, including ML researchers, software engineers, and data engineers, to define requirements and align on architectural decisions. Enable the junior engineers in the team to grow their careers by providing technical guidance and mentorship. Qualifications 4+ years of ML infrastructure experience. Experience building large-scale distributed multi-node GPU model training and/or high throughput, low latency serving use cases. Experience with training frameworks like PyTorch and OSS frameworks like Ray. Experience with GPU-accelerated inference using TensorRT, Ray Serve, or a similar framework. Experience working with cloud providers like AWS. Compensation There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. The salary range for this position is $196,000 to $319,000. A sign-on bonus may be offered as part of the compensation package. 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. #J-18808-Ljbffr