Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- 3 years of experience in development with JAX, PyTorch, or TensorFlow.
- 3 years of experience with Machine Learning algorithms, research methodologies, and Language Modeling.
Preferred qualifications:- 1 year of experience in a technical leadership role.
- Experience developing accessible technologies.
About the jobAs 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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Translate research into production-ready systems, driving the full lifecycle from rapid prototyping and large-scale dataset curation to model training, evaluation, and real-world deployment.
- Architect and advance multimodal foundation models spanning generative media (text, image, audio, and video) and rich cross-modal understanding to power next-generation Gemini Omni capabilities.
- Design, train, and scale advanced architectures leveraging techniques such as reinforcement learning (RL/RLHF /RLAIF) to enhance reasoning, steerability, and multimodal generation quality.
- Optimize model architectures and inference pipelines using quantization, distillation, pruning, and kernel optimization to achieve low-latency, cost-effective serving at scale.
- Partner cross-functionally with research scientists, product managers, and infrastructure teams to identify emerging technical opportunities and bring novel AI features to millions of users.
