Waabi jobs

Waabi is a leader in Physical AI, developing technology for autonomous transportation. We are seeking a Senior/Staff ML Ops Engineer to build and evolve our training infrastructure on Kubernetes, focusing on GPU scheduling, autoscaling, and multi-node distributed jobs. You will shape the developer-facing surface of our tools, shorten the development loop for ML engineers, and evangelize best-in-class tooling. This role involves strengthening our data and artifact layer, turning one-off scripts into durable, tested libraries, and making experiments legible through robust experiment tracking and model registries. You will also ship CI/CD for models, build comprehensive observability across the ML stack, and treat documentation and onboarding as a core product surface. Driving adoption through user empathy and prototyping is key, alongside ensuring platform reliability and building secure guardrails.

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Waabi is seeking experienced Software Engineers to join its AI Technical Strike Team. This team will focus on building and shipping AI tooling, agents, and workflows to maximize productivity across all departments at Waabi, from ML development to traditional software engineering and business functions. You will design, build, and integrate custom internal tools and extend third-party AI solutions, working closely with various teams to understand their needs and co-design AI-powered workflows. The role involves evaluating new AI technologies, implementing responsible AI guardrails, and driving tool adoption through documentation and support.

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This role focuses on designing and developing data-driven and machine-learned approaches for vehicle control problems, bringing modern ML to a domain traditionally solved with classical methods. You will develop learned models of vehicle behavior and dynamics, integrate them into closed-loop simulation, and apply machine learning to improve controller adaptation across vehicles and operating conditions. You will own problems end-to-end, from conceptualization and offline experimentation through simulation and on-vehicle validation, and build necessary data pipelines, evaluation metrics, and tooling to measure performance against classical baselines. This is an opportunity to be part of a multidisciplinary team using an AI-first approach to enable safe self-driving at scale and define how learning and classical control coexist in a safety-critical stack.

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