Boston Dynamics seeks a Software Quality Assurance Leader to build and establish the SQA function for the Atlas Humanoid Robot. In this role, you’ll be responsible for ensuring the reliability, safety, and performance of Atlas’s software systems - both on-robot and off-robot. Candidates will apply all of their organizational, operational, and technical expertise
Develop and execute comprehensive SQA strategies covering embedded systems, AI/ML components, control systems, and user interfaces. There should be a strong bias towards automation, but still be open to more manual processes when appropriate.
Build, lead, mentor, and manage a team of SQA engineers and technicians, fostering a high-performance culture of quality, collaboration, and continuous improvement.
Establish and monitor key performance indicators (KPIs) such as performance metrics (parts moved/minute), reliability metrics (mean time between intervention) and QA Execution cost metrics (time-to-qualify, financial cost).
Be the trusted voice of software quality. Provide data-driven reports on the quality of a software release, and apply sound judgement on quality risks to other engineering and business leaders.
Get your hands dirty. Be familiar with how to use Atlas, write and execute test plans, develop automated test cases, and set up workflows in Jira or other tools.
10+ years of experience in Software Engineering or Software QA.
3+ years in a technical lead or managerial role.
Hands-on engineering work in the past - such as Software Engineering or Mechatronics Engineering.
Strong understanding of software lifecycle and associated tools (Git, Jira, etc.).
Analytical expertise - ability to interpret and present statistical reports and understand the shortcomings.
Ability to translate organizational goals to QA deliverables.
Excellent communication, leadership, and interpersonal skills.
Strong preference for candidates with experience leading SQA on hardware systems such as robotics, automotive, or embedded devices.
Prior experience performing SQA techniques on ML/AI learned models.
Familiarity with functional safety standards for robotics.