Robotics Software Engineer

Ixian Robotics•San Francisco, CA

4d•Onsite

About The Position

Ixian Robotics is building intelligent machines to reindustrialize America. The company is based in San Francisco and backed by a16z and Accel. Ixian views robotics as a full-stack problem that involves software, hardware, machine learning, customer understanding, product design, supply chains, and economics. Deployment is central to the company’s approach to innovation. The Robotics Software Engineer will build the software connecting sensors, robots, and machine-learning systems. This includes the robot runtime, supporting infrastructure, and tools engineers and operators use to run and debug the system. The role works directly with physical hardware and investigates failures across the stack, making problems visible, tracing their causes, and developing systems that continue to work through repeated use. This is a full-time, on-site position in San Francisco. The base salary is $150,000–$200,000 per year, with equity of 0.1%–1%. Medical, dental, and vision coverage are offered.

Requirements

Experience building and maintaining software that interacts with hardware, sensors, or other timing-sensitive systems.
Strong Python skills and proficiency in C++ or Rust for performance-sensitive work.
Practical Linux experience and a systematic approach to profiling, testing, and debugging.
Sound judgment about interfaces, data integrity, and tradeoffs between an initial implementation and a maintainable system.
Ability to take a loosely defined problem through implementation and validation on real equipment.

Nice To Haves

Experience with robotics middleware, camera calibration, or sensor synchronization.
Experience serving machine-learning models under latency constraints.
Experience building data infrastructure or tools used by technicians and operators.

Responsibilities

Build reliable sensor and actuator interfaces, including synchronization, calibration, and low-latency communication.
Develop the robot runtime, including model inference, execution, safety limits, logging, and fault recovery.
Build data pipelines with clear schemas, automated validation, and useful search and inspection tools.
Profile and improve performance across Linux, networking, storage, camera pipelines, and compute.
Build repeatable deployment and test workflows with versioning, rollback, and telemetry sufficient to diagnose failures.
Work with hardware, machine-learning, and operations teams to bring up systems, reproduce failures, and eliminate recurring causes of downtime.