About this role
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.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google (https://www.google.com/about/careers/applications/benefits/).
Minimum qualifications:
- Bachelor’s degree in Computer Science, Mathematics, Applied Stats, Machine Learning, or equivalent practical experience.
- 8 years of experience in software development.
- Experience in Python or C++, algorithm design, and machine learning.
- Experience acting as a Technical Lead, guiding system architecture and milestone delivery.
Preferred qualifications:
- Experience with large-scale data pipelines, distributed systems, and ML frameworks (e.g. JAX).
- Experience working on deployed projects from proof-of-concept through to implementation.
- Experience driving execution across a 5-10 person engineering/research team. Ability to balance unknowns and ambiguity with forward progress.
- Expertise in earth observation, remote sensing, or related fields.
- Demonstrated engineering excellence and execution efficiency.
- Act as a Technical Lead for 2-4 engineers, owning technical workstreams end-to-end, planning milestones, and driving execution from proof-of-concept prototypes to production systems.
- Architect, build, and scale large-scale data pipelines, distributed training infrastructure, and low-cost, interactive-speed model inference systems.
- Run experiments and test hypotheses with researchers and engineers to evaluate modeling and systems improvements across the machine learning life cycle.
- Collaborate with team members across London and New York, adapting as project priorities shift across phases of development.