Staff/Lead Machine Learning Engineer, Behavior & Planning

Nuro · Mountain View, California (HQ)lead

En corto

▸Ingeniero de ML de alto impacto en comportamiento y planificación de vehículos autónomos.
▸Liderazgo técnico en modelos de vanguardia: LLM, RL, planificación con transformers, etc.
▸Proyecto real con robots en carreteras, en expansión global y multiplataforma.

Fluency in English is required for collaboration across global teams.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓Experiencia probada en machine learning aplicado a problemas de conducción autónoma.
✓Capacidad de liderar proyectos desde investigación hasta despliegue en vehículos reales.
✓Dominio de técnicas avanzadas: RL, IL, modelos generativos, transformers, VLM.
✓Experiencia con el ciclo completo del modelo: datos, entrenamiento, evaluación, inferencia.
✓Capacidad de colaborar con equipos de percepción, simulación y ML infra.
✓Aptitud para mentorear y aumentar el nivel técnico del equipo.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

Foundation ModelsWorld ModelsLLM/VLM ReasoningReinforcement LearningImitation LearningGenerative ModelsDiffusion ModelsTransformer-based PredictionTransformer-based PlanningSimulation & Evaluation

¿A quién escribirle en Nuro?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role We are looking for a Staff Machine Learning Engineer to be a technical leader on Nuro’s Behavior & Planning team. Our team owns how the Nuro Driver behaves on the road: prediction, decision making, and planning, and is responsible for turning Nuro’s large-scale driving data into safe, comfortable, and natural driving behavior. In this role you will bring strong, general machine learning expertise to some of the hardest problems in autonomy and drive them from research through to deployment on real vehicles. You’ll work at the frontier of applied ML spanning areas such as foundation and world models, LLM/VLM reasoning, reinforcement and imitation learning, generative and diffusion models, and transformer-based prediction and planning. You’ll apply this knowledge and experience to make our driving generalize as we scale across new geographies as we expand throughout the U.S. and globally, and across new vehicle platforms including robotaxi, personally owned vehicles, and delivery/logistics. This is a high-ownership role. You will set technical direction, lead initiatives that span multiple teams, and operate with a high degree of autonomy and minimal oversight. For the right person, it offers a clear path to grow into a management or tech-lead role as the team scales. If you love solving hard new problems and seeing your models drive real robots in the physical world, come join us! About the Work • Technical Leadership: Lead ML initiatives across the autonomy stack framing ambiguous problems, setting technical direction, and driving them from idea to on-road deployment with minimal oversight. • Applied ML at the Frontier: Design, train, and productionize state-of-the-art models across some or many of: foundation and world models, LLM/VLM reasoning, reinforcement and imitation learning, generative and diffusion models, and transformer-based prediction and planning. • Generalization & Scale: Build behavior and planning models that generalize to new cities and geographies (U.S. and global) and adapt to new vehicle platforms. • Cross-Functional Collaboration: Partner closely with Perception, Simulation & Evaluation, and ML Infra & Data, as well as the broader Autonomy and Research orgs, to develop holistic solutions to top autonomy challenges, align on priorities, and unblock shared initiatives. • End-to-End Ownership: Own the full model lifecycle: data, training, onboard inference, closed-loop and open-loop evaluation, and continuous on-road iteration. • Mentorship & Influence: Raise the technical bar of the team, mentor engineers and researchers, and shape roadmap and technical strategy beyond

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