Lead Software Engineer, Infrastructure Quality, Robotics, DeepMind
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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 job
We believe there are many problems in the world in which robotics could play a significant role in making it easier, faster and safer for people to get things done. We’re looking for roboticists, designers, hardware and software engineers to help us explore these possibilities, develop breakthrough technologies, and build new products that could help millions of people.
Artificial intelligence will be one of humanity’s most transformative inventions. At DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
Responsibilities
- Design and implement large scale software and hardware for major projects, supporting both conventional and on-robot testing, as well as manual and automated.
- Design and deploy a software release discipline, with flexible and powerful staging environments, and comprehensive regression checks for software, AI models, and robot hardware as it wears out over time.
- Define and implement engineering productivity metrics over the full engineering lifecycle, from code authoring to landing in production. Identify and eliminate bottlenecks, code debt, reduce productivity drag, and improve code health. Act as a team multiplier on the software health of the organization, particularly as it pertains to optimizing the Data/ML Flywheel.
- Lead agentic AI in our complex and unique ecosystem.
- Drive end-to-end system reliability and systems engineering for critical fleet management and data systems and the physical robot fleet.
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