About the job

In this role, you will be tasked with not only maintaining the library but proactively evolving it. You will move beyond simple bug fixing to explore experimental quantization algorithms, adding them to the library before customers even realize they need them.

You will operate in a unique environment where you must balance the agility of open-source software with the reliability required by Google-scale production. You will need to obsess over both quality (preserving model accuracy) and performance (optimizing runtime). You will need to be comfortable deep-diving into low-level profiles to debug TPU/GPU bottlenecks, while simultaneously possessing the soft skills to communicate effectively with partner teams in Google Deep Mind (GDM) and customer teams across Search and Ads. You will be defining how the world optimizes JAX models.

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 $174,000-$255,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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

• Design and implement new quantization features (e.g., post-training quantization (PTQ), quantized training (QT), and on-device machine learning (ODML) support)) to keep pace with the rapidly evolving JAX ecosystem.

• Proactively research and implement experimental quantization algorithms (e.g., int2 numerics, dual scale quantization, hadamard transformation) to lead customer adoption rather than just reacting to requests.

• Debug and optimize low-level performance issues. Use accelerated linear algebra (XLA) profiling tools (xprof) to analyze TPU/GPU execution traces, identify bottlenecks, and ensure the lowest-cost implementation of algorithms.

• Manage the health of the codebase across two fronts: resolving issues on the public repository and triaging high-priority bugs for internal partners.

• Write high-quality documentation, tutorials, and examples for the open-source community to lower the barrier to entry for new users.

Minimum qualifications

• Bachelor’s degree or equivalent practical experience.

• 2 years of experience programming in Python or C++.

• 2 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

• 2 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.

• 2 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization , data processing, debugging).

Preferred qualifications

• Experience with JAX transformations (vmap, pjit, grad) and the underlying XLA compiler stack.

• Experience reading high level optimizer (HLO) code to understand exactly how Python code translates to hardware execution is highly valued.

• Understanding of theoretical quantization and quantization techniques (PTQ, QAT, weight-only vs. activation) and low-precision numerics (int8, fp8, int4), and the mathematical implications of compression on model convergence.

• Ability to interpret low-level performance tools (e.g., xprof, Tensor Board) to identify padding issues, memory fragmentation, or SIMD utilization gaps, profiling and optimizing ML models on TPUs or GPUs.