Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development and with data structures/algorithms.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience with machine learning algorithms, tools, and libraries.
- Experience with building high-quality and reusable AI infrastructure, compilers, or performance engineering.
- Experience with stack-spanning systems and tools, from high-level Python to low-level C++.
- Understanding of the full user experience.
- Interest in keeping up-to-date on the latest AI models, tools, and techniques.
Want more jobs like this?
Get Software Engineering jobs delivered to your inbox every week.
About the job
Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
Our Core Machine Learning team's mission is to expand the possibilities of generative AI research and deployment. We do this by building libraries and tools that leverage the best of Alphabet and open-source technologies, centered around JAX.
Jax is used extensively, including the largest production Large Language Model (LLM) deployments at Google. JAX's flexibility and parallelism APIs make it particularly powerful for large-scale distributed training/inference. As a founding member of the team, you'll be responsible for helping define our work and how we can most effectively empower the JAX community. While the team's work will be centered around JAX, scope can and should extend to any part of the stack where we can be useful.
Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
Responsibilities
- Work across the tech stack to improve LLM training and inference performance on GPU.
- Add new features and publish high-performance open-source kernels.
- Partner with the JAX team to design and implement new JAX features, and collaborate directly with JAX power users to solve tricky problems and enable new workloads.
- Create smooth inter-operations between JAX and PyTorch (e.g., for data loading, hybrid models, or portability).
- Implement and benchmark reference JAX models and techniques, also inform new JAX features and improvements.