Senior Software Engineer, Performance Optimizations for LLM
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s 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. In this role you will be a key engineer contributing towards feature development and performance optimizations for LLM inference on TPUs.The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.
The US base salary range for this full-time position is $174,000-$252,000 + 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.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience programming in Python or C++.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- 3 years of experience with large language model (LLM) concepts, algorithms, and experience designing NLP solutions.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, and debugging).
Preferred qualifications:
- Experience with modern deep learning toolkits (e.g., JAX).
- Experience developing and optimizing LLMs.
- Experience working on GPUs or TPUs.
- Experience with latency, memory, compute, and quality tradeoffs as they apply to ML model architectures.
- Experience with low-level ML model optimization, new architectures, and tools.