Noc Soc Shift Lead in San Francisco

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We are looking for a highly experienced, hands-on RTL engineer with deep packet-networking and interconnect expertise to own critical on- and off-chip communication components for our custom AI accelerator platform. You will drive the microarchitecture and RTL implementation of scalable on-chip fabrics connecting high-bandwidth compute, memory, and I/O subsystems, as well as purpose-built off-chip interfaces and protocols that enable custom computing at scale. The ideal candidate has recently owned production RTL for networking or packet-switched datapaths such as Ethernet switches, NICs, SmartNICs, network accelerators, RDMA/RoCE systems, NoCs, or custom scale-up/scale-out fabrics. This is a senior engineering role with broad technical ownership and substantial direct RTL work. You will drive designs from requirements and performance analysis through microarchitecture, SystemVerilog implementation, verification closure, physical design convergence, silicon bring-up, and

About the Team Our DD Labs team builds real-time autonomous delivery systems. The Planning & Decision-Making group is investing heavily in deep reinforcement learning to move beyond classical planning, learning policies that generalize across novel driving scenarios, handle long-tail edge cases, and improve continuously from large-scale fleet data. Our models jointly handle prediction and planning in a single unified architecture. Our stack is pure JAX end-to-end: the same code you train with is the code that runs on the robot. No C++ rewrites, no TensorRT export. A new policy goes from training to on-vehicle deployment in minutes. About the Role As a Senior/Staff Deep RL Engineer, you will design, train, and deploy deep reinforcement learning policies that make real-time driving decisions for our autonomous vehicles. You will own the full lifecycle, from problem formulation and reward design through large-scale distributed training to on-vehicle inference. You'll help define how learned components compose with the rest of the autonomy stack to produce robust, shippable behavior. You’re excited about this opportunity because you will… Formulate complex driving tasks as RL problems with well-shaped reward functions and expressive state/action representations. Design and train model-based deep RL agents using GPU-accelerated simulation at massive scale, including improving the simulator itself. Build and maintain distributed training infrastructure in JAX across large compu