Job Type
Full Time
Job Details
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA. Minimum qualifications:
Preferred 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 leading ML design, fine tuning/reinforcement learning (RL), and debugging data issues (e.g., bias, train/inference mismatch, data quality).
- Experience with large language models, including 7b+ models, in a professional setting.
- Experience processing and working to understand natural language and large language models.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
- Experience as a manager.
About the job
Join the team shaping the future of conversational AI and language understanding at Google. The Gemini team is at the forefront of deploying large language models (LLMs) that power Google’s conversational AI experiences. As a member of the Gemini team, you will contribute to the advancement and application of cutting-edge generative AI technologies that power conversational experiences and revolutionize the way we interact with information.
Gemini’s mission is to be the forerunner of bringing this technology to users and pushing the boundaries of what is possible. In this role, you will help drive advancements in:
- Modeling techniques for reasoning, planning, tool chaining.
- Instruction tuning to make LLM follow user prompts
- Leveraging human feedback through reinforcement learning (RLHF)
- Targeted LLM-based synthetic data generation
- Empower LLMs to generate code that can be executed for plotting, solving equations, interactive data visualization and more.
- Making LLMs orchestrate over tools and backends.
- Retrieval over knowledge bases.
- Building automatic LLM-based offline evaluations.
- Increasing trust & safety of our models.
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
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form. - Drive innovation by translating cutting-edge ML research into impactful user-facing solutions.
- Push the boundaries of innovation and research across key Gemini dimensions - factuality and hallucinations, instruction following, coding generation capability, RLHF, differential privacy, handling long context, multimodal inputs and outputs, and more.
- Design and execute rigorous experiments, analyze results, and iterate on solutions to achieve optimal outcomes.
- Provide technical leadership on high-impact projects.
- Lead NLP solution design, optimize ML infrastructure, and guide model evaluation and debugging efforts.
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