Staff Software Engineer, GenAI, Data Quality

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.
• Experience in cross-functional collaboration and working across boundaries.
• Experience working with Python or C++.

Preferred qualifications:

• Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field.
• Familiarity with ML production tools and lifecycle.

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.

Power the next Large Language Model (LLM) advancements with data.

We are building Google-wide 1P tooling for onboarding very large rater workforces to meet the human data needs for all Gemini use cases. Our team builds and maintains platforms, scalable infrastructure services and applications in support of human computation, enabling the collection, storage and management of large-scale human-generated or human-augmented datasets used by teams in Google and Alphabet working on GenAI products and services. Our team has been instrumental in performing LLM/GenAI model evaluations and model fine-tuning (e.g., using SFT and RLHF techniques).

Our team has long experience enabling ML products via human data. We're the preferred human data solution for the ML platforms at Google.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

The US base salary range for this full-time position is $197,000-$291,000 + 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

• Provide technical leadership to the Crowd and Data Platform team as it executes on company priorities to improve tooling around 1P Machine Learning (ML) data needs for GenAI/LLM use cases.
• Improve the quality of data collections used for GenAI models training by identifying best practices through quantitative pilot studies and implementing the adoption of these practices by human data collection systems.
• Productionize and standardize methods developed by data scientists for high-quality data and ensure that these metrics are visible to the right stakeholders, meaningful, and actionable.
• Collaborate with horizontal infrastructure teams to monitor and report data quality at every stage of the data collection lifecycle, from collection design through training and model release.
• Work cross-functionally with Data Science, Product, UX/UX Researcher, Infra and Operations teams.