Research Scientist, Research, GenAI

San Francisco Fair Chance Statement

Applicants in San Francisco:
Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.

• Health, dental, vision, life, disability insurance
• Retirement Benefits: 401(k) with company match
• Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
• Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)
• Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
• Baby Bonding Leave: 18 weeks
• Holidays: 13 paid days per year

By applying to this position you will have an opportunity to share your preferred working location from the following:
Mountain View, CA, USA;
Kirkland, WA, USA;
Seattle, WA, USA;
San Francisco, CA, USA
.

• PhD degree in Computer Science, a related field, or equivalent practical experience.
• Experience with one or more of the following (e.g., computer vision, machine learning, natural language processing.
• Experience with software development in Python.
• One of more scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).

• 2 years of experience in coding.
• 1 year of experience owning and initiating research agendas.
• Experience with jax/pytorch, Python, and building/launching ML models and applied ML skills (e.g., Large Language Models (LLMs), Natural Language Processing (NLP)).
• Experience with quantitative research or investigative abilities for data analysis.
• Experience with Google infrastructure such as flume.

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll set up large‑scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies.

From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real‑world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects and from collaborations with research programs at partner universities and technical institutes all over the world.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world’s fast‑paced business needs.

The US base salary range for this full‑time position is $141,000-$202,000 + 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.

• Design, implement, and evaluate advanced ML models utilizing large‑scale datasets to analyze and predict human…