Robotics Software Engineer
Posted Updated
- Physical Intelligence is bringing general-purpose AI into the physical world
- We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future
- Achieving real-world performance requires extremely tight system latency, reliable sensor pipelines, and end-to-end engineering that makes perception and control loops work at real-time speeds
- As a Runtime Software Engineer, you’ll engineer the low-latency, high-throughput systems that underpin our physical intelligence model
- You won’t be designing ML models - you’ll be the person who makes them run flawlessly in production, optimizing every layer from OS to camera pipeline to networking
- You’ll collaborate closely with researchers, platform engineers, and robotics operators to identify bottlenecks and extract maximum performance from the entire system
- The Runtime team is responsible for building the core platform that Pi’s robots, sensors, and evaluation pipelines rely on
- The team spans Linux systems engineering, camera and sensor pipelines, robot actuator controllers, networking, real-time IO, and performance tooling
- They ensure our ML models and control systems operate under strict latency budgets and are robust under real-world conditions
- Own Real-Time Pipelines: Engineer low-latency, high-reliability sensor and actuator pipelines across Linux, drivers, and middleware
- Optimize System Performance: Profile and optimize across compute, I/O, memory, scheduling, networking, and storage to meet real-time constraints and increase throughput
- Build OS-Level Capabilities: Extend or modify Linux components, drivers, and scheduling to achieve deterministic behavior under load
- Streaming & Video Systems: Develop and optimize real-time video streaming systems where frame timing and packet scheduling matter
- Reliability & Debugging: Build tooling for profiling, tracing, and debugging timing issues across distributed systems and hardware interfaces
- Cross-Functional Collaboration: Work with researchers, hardware engineers, and operations teams to integrate optimized pipelines into production workflows
- Ability to collaborate deeply with researchers and platform engineers to translate high-level model requirements into real-world system performance
- Experience with profiling tools (perf, tracing, eBPF, GPU profilers, network analyzers) and comfort diving into complex performance issues
- Ability to optimize across the entire stack - kernel scheduling, drivers, networking, GPU/CPU workloads, video frameworks, and distributed components
- Experience with Linux systems programming (syscalls, drivers, kernel parameters, scheduling, memory/IO subsystems)
- Strong programming skills in C++, Rust, or Python, with experience building and optimizing production software
- A mindset oriented around determinism, throughput, frame budgets, jitter minimization, and real-time correctness
- Background in real-time or near–real-time systems, VR/AR, video pipelines, 3D engines, or streaming systems where latency budgets are strict
- Experience with VR/AR platforms or low-latency 3D engines
- Camera system expertise (synchronization, capture pipelines, codecs, GPU offload)
- Streaming/video conferencing stack experience (WebRTC, real-time transport optimizations)
- Background in robotics, autonomous systems, SLAM pipelines, or perception systems (implementation, not research)
- Familiarity with distributed systems that process real-time data flows
- Expertise in kernel-level engineering, device drivers, or high-performance networking