Senior Software Engineer, AI/ML, ML Training

Google•Kirkland, WA

7h•$174,000 - $252,000

About The Position

Google software engineers develop technologies used by billions of people and work on projects critical to Google’s needs, with opportunities to move across teams and projects as the business evolves. The role calls for versatility, leadership, and willingness to tackle problems across the full stack. This position is on the JAX Models and Performance team, which supports and optimizes Google Cloud Platform customers’ generative AI workloads for key model architectures. The team develops open-source, high-performance JAX implementations for large language models and diffusion models, collaborating with DeepMind and Core ML teams to optimize workloads on GCP AI Hypercomputer, including TPUs, GPUs, and CPUs. The team is part of the ML Training team in the Core ML organization. Individual pay is determined by job-related skills, experience, and relevant education or training. The listed U.S. compensation is $174,000–$252,000 USD, plus a 15% target bonus, equity, and benefits.

Requirements

Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in one or more programming languages.
3 years of experience testing, maintaining, or launching software products, including 1 year of experience with software design and architecture.
3 years of experience with end-to-end machine learning, such as model deployment, model evaluation, data processing, or debugging.
3 years of generative AI or performance analysis experience.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
5 years of experience with data structures and algorithms.
2 years of experience with ML accelerators (TPUs, GPUs) and their performance tuning.
2 years of experience with Mixture of Experts (MoE) or diffusion models performance optimization.
Experience building and releasing software artifacts such as pip wheels and Docker images.

Responsibilities

Write and test product or system development code.
Collaborate with peers and stakeholders through design and code reviews to ensure best practices, including style, code checking, accuracy, testability, and efficiency.
Contribute to documentation or educational content and adapt it based on product or program updates and user feedback.
Triage product or system issues and debug, track, and resolve them by analyzing their sources and effects on hardware, network, or service operations and quality.
Design and implement solutions in one or more specialized machine learning areas, leverage machine learning infrastructure, and demonstrate expertise in a chosen field.