About this role
At Meta, we're building the future of human connection and the technology that enables it, continuously inventing for the next generation of experiences. To move toward advanced machine intelligence, we're enabling AI to go beyond vision and interact with the physical world.
As we move closer to a future with intelligent robots and advanced AI models, we're hiring talent across robotics hardware, system software, machine perception, and artificial intelligence. You'll tackle open problems in robotics and AI, with the opportunity to shape new ways people connect around the world.
Meta Robotics Studio is seeking a Reliability Engineer who will define and implement reliability processes and systems to ensure the long-term performance and robustness of prototypes and production devices. This role is responsible for developing and executing reliability test plans, validating results, and driving continuous improvement in collaboration with design, test, and manufacturing teams.
Responsibilities
Develop and execute reliability test plans and validation processes
Define reliability metrics and reporting standards for the program
Analyze failure modes, perform root cause analysis, and drive corrective actions
Collaborate with design, test, and manufacturing teams to improve reliability
Establish processes for proactive risk identification and mitigation
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Bachelor’s degree or equivalent experience in Engineering or a related field
Experience collaborating across design, test, and manufacturing functions
5+ years of experience in reliability engineering, testing, and validation
Experience analyzing test data and failure modes to drive engineering decisions (e.g., root cause analysis, FMEA, statistical methods)
Experience developing reliability processes and metrics Experience working in a highly collaborative, cross-functional environment while communicating complex concepts clearly and succinctly
Master's degree or equivalent experience in Engineering, Science, or a related field
Experience with working in an ambiguous environment to solve problems
Experience optimizing design of existing products or devices