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Mind Robotics is building Physical AI systems for industrial deployment, starting with factory automation. The company focuses on creating robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world environments.
As Tech Lead for Robotics Runtime & Middleware, you will own the core platform layer that enables robots to perceive, decide, and act under hard real-time constraints. This is an individual-contributor-heavy role where you spend significant time in the codebase making critical latency and correctness tradeoffs yourself.
Key responsibilities include:
- 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), including batching, quantization, hardware acceleration, and model accuracy vs. control-loop latency tradeoffs
- Architect multi-sensor synchronization and fusion across different clocks and cadences for coherent closed-loop control
- Make and own hard technical decisions on middleware choice, process/thread architecture, and real-time guarantee requirements
- Set technical standards and conduct deep design/code reviews across the runtime engineering team
- Prototype and de-risk new architecture directions (middleware, compute hardware, model-serving approaches) before team-wide rollout
- Work directly with modeling and research teams to understand policy requirements and ensure the platform delivers on latency, synchronization, and action representation needs
Required qualifications:
- Strong systems programming in Python and/or Rust
- Hands-on experience with robotics middleware (ROS2, DDS implementations like Fast DDS/CycloneDDS, or Zenoh), including custom message types, QoS tuning, and multi-node communication
- Real-time systems expertise: scheduling, latency budgets, jitter, soft vs. hard real-time guarantees, and RTOS concepts
- Embedded/edge Linux experience: cross-compilation, device drivers, resource-constrained execution
- Multi-sensor synchronization: time sync protocols (PTP/gPTP), sensor fusion pipelines, clock drift/skew handling
- Model deployment/inference optimization: ONNX Runtime, TensorRT, quantization, batching strategies for edge inference
- Control systems and motion primitives knowledge to reason about how software architecture affects closed-loop robot behavior
- Track record of shipping real-time systems from prototype to reliable, continuous operation on physical hardware
- Strong technical judgment on research velocity vs. runtime reliability tradeoffs
- Experience making build-vs-buy decisions on middleware and compute hardware
- Ability to work directly with research/modeling partners to translate requirements into architecture
Nice-to-have: simulation environments (Isaac Sim, MuJoCo, Gazebo), functional safety concepts, autonomous vehicle or industrial IoT background, experience scaling runtime platforms to production, informal technical mentorship experience.
