This role suits an experienced engineer who wants to own the hardest problems in real-time robotics software and onboard model serving, and ship them personally. The Tech Lead, Robotics Runtime & Middleware position is based in Palo Alto at Mind Robotics, focusing on physical AI systems for industrial deployment.
Role Overview
Mind Robotics seeks a Tech Lead for Robotics Runtime & Middleware in Palo Alto. This is an individual contributor-heavy position focused on building generalized physical AI and robotic systems for industrial environments. The candidate will design the runtime platform that enables robots to perceive, decide, and act under strict real-time constraints.
Key Responsibilities
Design and construct the robotics runtime and middleware layer, covering topic and message architecture, QoS policies, and multi-process communication under strict latency limits.
Manage the inference-serving path for onboard models, including batching, quantization, hardware acceleration, and balancing model accuracy with control-loop latency.
Architect synchronization and fusion for multi-sensor inputs arriving on different clocks and cadences to maintain coherence for closed-loop control.
Make technical decisions regarding middleware selection, process and thread architecture, and real-time guarantees for subsystems.
Establish technical standards and conduct design and code reviews for runtime engineering.
Prototype and evaluate new architectural directions involving middleware, compute hardware, and model-serving approaches prior to broad team adoption.
Collaborate directly with research and modeling teams to align runtime delivery with policy requirements regarding latency, synchronization, and action representation.
Requirements
Systems programming skills in Python and/or Rust.
Practical experience with robotics middleware such as ROS2, DDS implementations, or Zenoh, encompassing custom message types, QoS tuning, and multi-node communication.
Knowledge of real-time systems, including scheduling, latency budgets, jitter, soft and hard real-time guarantees, and RTOS concepts.
Experience with embedded and edge Linux, covering cross-compilation, device driver basics, and resource-constrained execution.
Multi-sensor synchronization expertise using time sync protocols, sensor fusion pipelines, and handling clock drift across distributed compute.
Model deployment and inference optimization proficiency using ONNX Runtime, TensorRT, or similar tools, along with quantization and batching strategies.
Understanding of control systems and motion primitives to evaluate how software architecture decisions impact closed-loop robot behavior.
Demonstrated history of advancing real-time systems from prototyping to reliable operation on physical hardware.
Technical judgment regarding tradeoffs between research velocity and runtime reliability.
Ability to make build-versus-buy decisions for middleware and compute hardware.
Communication skills to work alongside research partners without intermediary layers.
Nice to Have
Familiarity with simulation environments such as Isaac Sim, MuJoCo, or Gazebo.
Exposure to functional safety concepts for physical systems.
Background in autonomous vehicles, industrial IoT, or related domains with real-time and embedded constraints.
Experience scaling a runtime platform from a few robots to multiple production units.
Prior experience providing technical mentorship or informal guidance to other engineers.
Skills and Tools
Python
Rust
ROS2
Fast DDS
CycloneDDS
Zenoh
RTOS
Embedded Linux
PTP/gPTP
ONNX Runtime
TensorRT
Isaac Sim
MuJoCo
Gazebo
How to Apply
Interested candidates should apply on the official company careers page.
