Senior/Staff Deep RL Engineer for Real-Time Autonomy

DoorDash, Inc.; San Francisco (CA); Presencial; USD 168.000 - 247.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Equity grants

Comprehensive benefits

Descripción de la vacante

DoorDash’s DD Labs team is seeking a Senior/Staff Deep RL Engineer to design, train, and deploy deep reinforcement learning policies for real-time autonomous driving decisions. You will own the full lifecycle from problem formulation through large-scale distributed training to on-vehicle inference.

We value hands-on RL with scale, a solid foundation in deep learning, and proficiency in JAX, enabling efficient, end-to-end training and deployment on autonomous systems.

Formación

• BS/MS/PhD in CS, EE, Robotics, or a related field, with a strong foundation in reinforcement learning and deep learning.
• Hands-on experience training RL agents at scale, ideally in robotics, autonomous driving, or other real-time decision-making domains.
• Proficiency in JAX or a similar functional ML framework; comfort with JIT compilation, vectorized environments, and GPU-accelerated simulation.

Responsabilidades

• Design, train, and deploy deep reinforcement learning policies that make real-time driving decisions for autonomous vehicles.
• Own the full lifecycle, from problem formulation and reward design through large-scale distributed training to on-vehicle inference.
• Help define how learned components compose with the rest of the autonomy stack to produce robust, shippable behavior.
• Build and maintain distributed training infrastructure in JAX across large compute clusters.

Conocimientos

Reinforcement learning

Deep learning

JAX

Distributed training

Educación

BS/MS/PhD in CS, EE, Robotics, or related field

Herramientas

JAX

Descripción del empleo

DoorDash’s DD Labs team is seeking a Senior/Staff Deep RL Engineer to design, train, and deploy deep reinforcement learning policies for real-time autonomous driving decisions. You will own the full lifecycle from problem formulation through large-scale distributed training to on-vehicle inference.

We value hands-on RL with scale, a solid foundation in deep learning, and proficiency in JAX, enabling efficient, end-to-end training and deployment on autonomous systems.