Senior Robotics ML Systems Engineer

Reactor; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Visa sponsorship

Relocation support

Health, dental, and vision coverage

Equity

Job summary

Reactor in San Francisco is building an inference platform for real-time world models. This hands-on role sets the technical direction of Reactor's robotics work from the ground up, ensuring customer models run reliably on real robots within strict latency budgets.

We're looking for a generalist across ML, systems and robotics who understands policy behavior at inference time, and how batching, scheduling and network paths contribute to end-to-end latency.

Qualifications

• Experience running learned policies or ML models on real robots or autonomous systems.
• Strong ML understanding of robot policies and world models at inference time, not training them.
• Depth on inference systems: batching, scheduling, GPU utilization, p99 latency vs throughput.
• Strong production-level C++ and Python coding skills.
• Experience with ROS/ROS2 and DDS in robotic middleware.

Responsibilities

• Serve robot policies on GPUs under robot-grade latency budgets and profile the full path from input to output.
• Build the integration layer between Reactor and robot stacks: ROS/ROS2 nodes, bridges, transport, and low-latency channels.
• Use world models for policy evaluation and tune rollouts for throughput and turnaround time.
• Bring teleoperation and human-in-the-loop experiences to real-time transport, including synchronization and failover.
• Debug with customer robotics teams across sensing, network, inference, and control loops.
• Help shape the technical roadmap and set engineering standards for the team.

Skills

C++

Python

ROS/ROS2

Latency budgeting

Robot policies

World models

Inference systems

GPU utilization

Tools

DDS

TensorRT

CUDA

Triton

Job description

Reactor in San Francisco is building an inference platform for real-time world models. This hands-on role sets the technical direction of Reactor's robotics work from the ground up, ensuring customer models run reliably on real robots within strict latency budgets.

We're looking for a generalist across ML, systems and robotics who understands policy behavior at inference time, and how batching, scheduling and network paths contribute to end-to-end latency.