Minimum qualifications:- Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
- 8 years of experience programming in a general purpose coding language (e.g., C, C , Java, JavaScript, or Python).
- Experience in a people management, supervision/team leadership role.
Preferred qualifications:- Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or related technical fields.
- 3 years of experience in systems development engineering, system reliability engineering, software engineering with a focus on operations, or advanced systems administration.
About the jobThis is a dedicated Software Quality and Engineering Productivity leadership role. In this role, you will be an expert whose primary passion and career focus is building, owning, and scaling the quality of software releases and AI model validation on standard and custom hardware. You will play a major role in influencing the velocity at which our team solves Artificial General Intelligence (AGI) in the physical world. As our software and hardware infrastructure grows to support the increasing scale of robotics research, we need to continuously build and upscale our testing, reliability, validation, release strategy, and overall engineering productivity and velocity.
Robotics infrastructure is uniquely difficult. You will be solving for non-deterministic hardware wear-and-tear, complex large-scale model deployment, fast inference, and the massive data requirements of AGI. You will be experienced with software quality and release strategy on custom or experimental hardware. Your work will directly determine how many iterations our engineers and scientists can run per day, week, and year, while holding a high quality bar.
US: $262000 - $365000 (USD) 25% bonus target bonus equity benefits
Responsibilities - Design, implement, and own the long-term roadmap and execution for large scale software and HWITL testing environments, conventional and on-robot testing, as well as manual and automated.
- Design, deploy, and manage a robust software release discipline, with flexible powerful staging environments, and comprehensive regression checks for software, AI models, and robot hardware over time.
- Define productivity metrics such as code in production, bottlenecks, code debt, reducing, and improving code health, and optimize the data/ML Flywheel, from ingestion and labeling to model evaluation and deployment.
- Lead application of agentic AI, including automating root-cause analysis of HWITL failures, edge-case scenarios, or data quality or AI model performance problems.
- Drive end-to-end system reliability for software systems and robot fleets. Partner with external hardware vendors and internal teams to co-develop, integrate, and test joint software and hardware releases.
