Research Software Engineer, Google Research

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 5 years of experience with software development in one or more programming languages.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• Experience with LLM training and generative models.
• Experience using Python libraries and frameworks.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
  
• 5 years of experience with data structures/algorithms.
• 1 year of experience in a technical leadership role.
  
• Experience developing accessible technologies.
  
• Expertise in machine learning and reinforcement learning.
• Publications in machine learning conferences such as NeurIPS, ICML, ICLR, TACL, ACL, NAACL, EMNLP, COLM.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. 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’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. 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 team measures and improves key capabilities like multi-turn, factuality, and search for Gemini, working at the frontier of post-training for large foundational models. You will research new approaches for analyzing, evaluating and training LLMs to enhance their multi-turn capabilities.

In this role, you will devise innovative solutions for complex modeling and evaluation challenges in a fast-moving environment where adaptability is key to meeting tight deadlines for critical launches. You will collaborate closely with research and engineering teams across Gemini, using your findings to inform the development roadmap and presenting your work regularly to both the team and executive stakeholders.

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 $166,000-$244,000 + 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

• Scope and drive research efforts to improve complex frontier Gemini capabilities, such as multi-turn, factuality and tool-use.
  
• Review the latest literature to guide research and experimental directions.
  
• Curate and generate data to evaluate and improve Gemini capabilities. Design and implement both human and automated evaluation strategies.
  
• Design and conduct supervised fine-tuning and reinforcement learning experiments to improve the performance of Gemini capabilities such as multi-turn and factuality.
  
• Collaborate with partners and product functions to deliver new model capabilities to production.