Staff Software Engineer, Generative AI Performance, Distributed Cloud

Key Skills

Aartificial intelligence, Algorithms, Data Analysis, Data structuring, Machine learning techniques, Natural Language Processing (NLP), TensorFlow

Job Description

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 in the Machine Learning field.
• 5 years of experience with performance, systems data analysis, visualization tools, or debugging.

Experience with distributed systems.

Preferred qualifications:

• Experience with Natural language processing, TensorFlow, Vertex AI, coding in Python or Go.
• Experience in defining roadmaps and collaborating across organization boundaries.
• Experience in developing ML algorithms or using ML algorithms for various problems.
• Experience in understanding the pipelines necessary for setting up the training and serving (Terraform).

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 mission is to deliver the Google Distributed Cloud Infrastructure for AI serving, fine tuning, and training Large Language Models (LLM) and traditional ML models.

Google Distributed Cloud (GDC) is a new platform offering connected, hosted, and software only solutions. Host platform offers air-gapped hardware and software solution, managed by Google or a trusted partner, for the most sensitive workloads. Connected platform offers Google-managed hardware and software solutions designed for low latency, data residency, and hybrid workloads. The goal of the team is to bring cutting edge Gemini models to on-prem and to run efficiently.

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 $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

• Fine tune Large language models using PEFT and LoRA. Build optimization techniques for LLMs serving on GPUs.
• Build alignment with peer engineering teams and influence roadmaps of GDC AI. Enable LLMs to run on GDC efficiently for large context sizes.
• Conduct performance analysis and efficiency tuning of LLM model serving. Evaluate and analyze RAG solutions.
• Utilize the LLM to define customer experience solutions. Collaborate across organizations and across Product Areas (Vertex, Cloud Networking) in bringing these solutions.
• Be able to open source where possible and contribute to the ecosystem.