Google
Research Quality Assurance Engineer, DeepMind
Found: Today
Research Quality Assurance Engineer, DeepMind
At Google DeepMind our mission is to build the world's first general-purpose learning agent. Central to this mission is the complex task of measuring the intelligence of our prototypes. As a Software Engineer, you will be working with the cutting edge AI agents developed by our exceptional team of Machine Learning and Neuroscience research scientists. Your responsibilities will include everything from creating systems for agent testing using 2D and 3D games to developing test problems within physics simulators. You will create graphical visualization of results, build competitive agent leaderboards and test new algorithms on robots. To succeed in this role you will need to have a strong foundation in software engineering and enjoy working on a wide range of challenging problems within a mission-driven team. As a part of the GDM Research Quality Team, a multidisciplinary group supporting multiple units across DeepMind, you will focus on delivering excellence in quality across a wide range of projects, spanning software and infrastructure testing to data and rating quality efforts. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits.
Minimum qualifications:
- Bachelor’s degree in Computer Science, Information Technology, or related technical field, or equivalent practical experience.
- 8 years of experience with one or more general purpose programming languages including but not limited to: Java, C/C++, Python, Objective C, JavaScript, or Go.
- Experience in Quality Assurance (QA), Quality Engineering (QE), DevOps, engineering productivity, continuous integration/continuous deployment (CI/CD), or product quality.
- Experience with test automation development.
Preferred qualifications:
- MBA or Master's degree in engineering, computer science, or related technical field.
- 6 years of relevant work experience (e.g., statistician/computational biologist/bioinformatician/data scientist/product analyst), including deep expertise and experience with statistical data analysis such as linear models, multivariate analysis, causal inference, sampling methods.
Responsibilities
- Test and validate internal platforms, tools, and AI systems end-to-end, from data ingestion and processing pipelines to model evaluation and deployment.
- Lead production release testing processes, including co-ordinating with software teams to build test plans, create documentation, and execute manual and automated tests.
- Design, build, and scale automated testing frameworks, CI/CD integrations, and data validation pipelines to ensure the continuous delivery of high-quality research data.
- Act as a primary advocate for quality across the full-stack, applying software engineering best practices and data science techniques to identify, measure, and resolve complex defects.
- Work cross-functionally with software engineers, researchers, and data scientists to define quality requirements, build shared testing infrastructure, and validate research outputs.
