Tech Lead, Real-Time Robotics Runtime & Middleware

Mind Robotics; Palo Alto (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Mind Robotics is building a runtime platform for real-time, physical AI on factory-floor robots. We seek a Tech Lead to own the robotics runtime/middleware, optimize latency, and guide model-serving paths for onboard AI.

You will design multi-sensor synchronization, QoS policies, and hardware-aware architectures in collaboration with research and modeling teams. You will directly influence decisions on middleware, compute hardware, and deployment strategies to ensure deterministic, reliable

Qualifications

• Strong systems programming in Python and/or Rust.
• Hands-on experience with robotics middleware — ROS2 (rclcpp/rclpy, DDS implementations like Fast DDS/CycloneDDS, or Zenoh), including designing custom message types, QoS tuning, and multi-node communication patterns.
• Real-time systems experience: understanding of scheduling, latency budgets, jitter, and the difference between soft and hard real-time guarantees; comfort with RTOS concepts.
• Experience with embedded/edge Linux — cross-compilation, device driver basics, and resource-constrained execution (CPU/memory/power budgets).
• Multi-sensor synchronization: time sync protocols (PTP/gPTP), sensor fusion pipelines, and handling clock drift/skew across distributed compute on a single robot.
• Model deployment/inference optimization: exporting and running models via ONNX Runtime, TensorRT, or similar; quantization and batching strategies for action-model inference on edge compute; understanding of how model latency translates into control-loop constraints.
• Experience with control systems and motion primitives — enough to reason about how software architecture decisions (message latency, action chunking, control frequency) affect closed-loop robot behavior.
• Track record of taking real-time systems from prototype to reliable, continuous operation on physical hardware.
• Strong technical judgment on tradeoffs between research/experimentation velocity (fast iteration, permissive interfaces) and runtime reliability (determinism, fault tolerance, safety margins).
• Comfort making build-vs-buy calls on middleware and compute hardware.
• Clear communicator who can work directly with research/modeling partners to translate model requirements into runtime architecture, without a layer of translation in between.

Responsibilities

• Design and build the robotics runtime/middleware layer – including topic/message architecture, QoS policies, and multi-process communication under tight latency budgets.
• Own the inference-serving path for onboard models (VLA/action-expert policies): batching, quantization, hardware acceleration, and the tradeoffs between model accuracy and control-loop latency.
• Architect multi-sensor synchronization and fusion arriving on different clocks and cadences, kept coherent enough for closed-loop control.
• Make and own the hard technical calls on middleware choice, process/thread architecture, and how much real-time guarantee any given subsystem actually needs.
• Set technical standards and do deep design/code review across runtime engineering.
• Prototype and de-risk new architecture directions (new middleware, new compute hardware, new model-serving approaches) before they become team-wide commitments.
• Work directly with modeling/research to understand what a policy actually needs from the runtime (latency, synchronization, action representation) and make sure the platform delivers it.

Skills

Python

Rust

ROS2

Real-time systems

Latency optimization

C++

ONNX/TensorRT

Embedded Linux

Multi-sensor fusion

Tools

ROS2/DDS (Fast DDS/CycloneDDS)

Zenoh

RTOS concepts

Cross-compilation

Job description

Mind Robotics is building a runtime platform for real-time, physical AI on factory-floor robots. We seek a Tech Lead to own the robotics runtime/middleware, optimize latency, and guide model-serving paths for onboard AI.

You will design multi-sensor synchronization, QoS policies, and hardware-aware architectures in collaboration with research and modeling teams. You will directly influence decisions on middleware, compute hardware, and deployment strategies to ensure deterministic, reliable