Robotics Software Engineer

Responsibilities

• Maintain a deep understanding of the full robotics software stack; profile and optimize data flows to minimize end-to-end latencies across components.
• Design and implement logging and monitoring frameworks to facilitate rapid debugging and provide visibility into system dataflows.
• Develop testing suites and validation pipelines specifically designed to identify robot degradation and anomalies.
• Design and execute experiments for system identification to refine simulation models and controller performance.
• Build streamlined deployment systems for machine learning models, prioritizing intuitive tooling that simplifies prototyping and minimizes friction for research teams.
• Author and maintain high-performance, production-grade C++ codebases adhering to industry best practices, and use Python for tooling, data analysis, and scripting.
• Proactively identify bottlenecks in performance and scalability, driving iterative enhancements to the software architecture.
• Design and implement safety functions while ensuring their compatibility with machine learning models.

Qualifications

• Education: BS, MS or higher in Computer Science, Robotics, Computer Engineering, or a related technical field.
• C++ Expertise: 3+ years of professional experience writing production-quality C++, with a focus on memory management, multi-threading, and real-time constraints.
• Robotics Frameworks: Hands-on experience with ROS2 or similar robotic middleware, including custom message generation and lifecycle management.
• Systems Engineering: Strong understanding of Linux systems, including profiling tools to identify and resolve latency bottlenecks.
• ML Deployment: Experience deploying and optimizing machine learning models (TensorRT, ONNX) onto edge devices or robotic platforms.

Preferred Qualifications

• Safety Critical Systems: Experience implementing safety functions or working with functional safety standards.
• Math & Physics: Strong foundational knowledge of system identification, control theory, and rigid body dynamics.
• Infrastructure & DevOps: Experience building automated testing pipelines, CI/CD for hardware, and custom logging/telemetry/visualization stacks.
• Simulation: Proficiency in using simulation environments for system validation and testing.