Software Platform Engineer

Gradient Robotics; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Gradient Robotics seeks a Software Engineer to move data from AI models to actuators, building real-time pipelines that deliver hundreds of MB at millisecond latency. You’ll work from kernel and firmware up through controls and perception, aiming to make the ML model the sole stochastic component.

Expect to ship production-quality code to robots daily, optimize data flows, and contribute to deterministic control loops.

Qualifications

• 1+ years of software engineering experience near hardware; strong project or internship exposure welcomed.
• Proficient in Rust, C++, or Python with understanding of OS and multithreading.
• Experience building production systems or internal tools; demonstrated timing/concurrency debugging.

Responsibilities

• Ship performance-critical code to real robots daily.
• Learn the full data flow from camera frames to perception, planning, control commands and actuator feedback.
• Contribute to real-time control loops to keep high-rate control deterministic alongside vision inference.
• Build parts of the perception data path to move high-bandwidth streams with minimal latency and no silent drops.
• Push down latency and tighten the stack for faster, more reliable operation.
• Create tooling and instrumentation to observe and debug real-time behavior across controls and vision.

Skills

Rust

C++

Python

OS concepts

Multithreading

Hardware proximity

Production systems

Tools

Bazel

Nix

CI/CD

HIL testing

Job description

About the Company

The datacenter buildout is the largest industrial project in human history. Gradient builds the autonomous robots that make it possible.

Partnered with the world's largest AI infrastructure companies and backed by the industry's best investors, we move fast and build full-stack systems that matter.

About the role

We're looking for a Software Engineer to help move data from AI models to actuators. You'll work close to the hardware, from kernel and firmware up through the controls and perception layers, contributing to real-time pipelines that move hundreds of megabytes at single-digit millisecond latency. Vision inference, control loops, and actuation all live on the same clock, and you'll help keep them there. The goal: bring visibility and determinism into the end-to-end inference pipeline so the ML model is the only stochastic component. We iterate fast (multiple robot generations in months, not years), so the code you ship lands on real machines doing real precision work almost immediately.

Responsibilities

• Ship performance-critical code to real robots daily

• Learn the full data flow: camera frames in, perception and planning in the middle, control commands and actuator feedback out, and the systems that carry them

• Contribute to the real-time control loop: help keep high-rate control running deterministically alongside vision inference

• Build pieces of the perception data path: move high-bandwidth camera streams into the CV and ML models with minimal latency and zero silent drops

• Push down latency and tighten the stack to make the system faster, more deterministic, and more reliable

• Bring visibility into the pipeline: build tooling and instrumentation that make real-time behavior across controls and vision observable and debuggable

Minimum Qualifications

• 1+ years of software engineering experience building systems close to hardware (exceptional new grads with strong project or internship experience are welcome to apply)

• Strong programming fundamentals in at least one of Rust, C++, or Python, with solid understanding of operating systems and multithreading

• Experience building things: production systems, internal tools, student teams, or personal projects that worked

• Debugged real timing, concurrency, or hardware issues, even in a project setting

• Comfort with ambiguity and a strong learn-by-doing instinct

• Able to work on-site in San Francisco 5 days/week (6 days/week if needed during crunch time), embedded in the team

Preferred Qualifications

• Linux kernel or embedded systems experience

• Exposure to build and test infrastructure: Bazel, Nix, HIL testing, or CI/CD

• Experience with real-time control systems: motor control, high-rate control loops, or robot controllers

• Computer vision pipeline experience: camera drivers, image transport, GPU inference, or sensor synchronization

• Tracing and profiling experience: ftrace, flamegraphs, eBPF

• Comfort using AI tools to multiply your own output