Research Engineer, Response Quality/Hermes at DeepMind

Mountain View, California, United States -

Full Time

Start Date

Immediate

Expiry Date

03 Jun, 26

Salary

211000.0

Posted On

05 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Model Alignment, SFT, Reinforcement Learning, Reward Modeling, Data Distillation, Evaluation Techniques, Agentic Frameworks, LLM Behavior Steering, System Instructions, Multimodal Synthesis, Multi-turn Reasoning, Python, Spark, Distributed Training, Research Execution, Product Impact

Industry

Research Services

Description

Research Scientist / Research Engineer, Response Quality/Hermes Mountain View, CA At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. Snapshot This will be an overview of your team. At Google DeepMind, we are a multidisciplinary team of engineers and scientists driving core contributions to Gemini’s post-training evolution. Our mission is to transform Gemini from a "one-shot" answer engine into an adaptive, collaborative partner with rich response formats. Our work spans the full stack of model alignment and interaction design, including: Advanced Modeling Interventions: Developing and scaling SFT, RL, and Reward Modeling (RM) techniques specifically designed to enhance multi-turn reasoning and collaborative behavior. Next-Gen Evaluation: Building autonomous user-agent evaluation techniques to simulate complex, multi-turn interactions and quantify the quality of human-AI exchange. Rich Multimodal Synthesis: Moving beyond "wall-of-text" responses by integrating images, charts, and interactive widgets to create a more expressive and functional user experience. About us Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority. The role Overview of the role As a Research Engineer/Scientist on the Gemini Post-Training team, you will bridge the gap between core model alignment and the next generation of interactive AI. You will be re-architecting how Gemini interacts, reasons, and collaborates with users over complex, multi-turn exchanges. Your work will involve building high-scale distillation pipelines, developing metrics and evaluation techniques, taking advantage of latest development on agentic framework and autonomous user agents, and designing custom system instructions (SIs) that redefine model behavior, and finding the right training recipes to impact Gemini. This is a high-impact role where your contributions to SFT and Reinforcement Learning will directly shape the multimodal, widget-rich future of the world’s most capable AI. Key responsibilities Add up to 5 responsibilities Models: Improve model quality for understanding and generation through applying and developing cutting edge modeling interventions, multi-turn reasoning, and rich-format multimodal synthesis. Data: Unlock new information-seeking and collaborative capabilities through large-scale data distillation and processing, bridging pre-training and post-training. Evals: Build on top of agentic frameworks to develop better evaluation methods (human, auto-raters, and automated metrics) that measure the quality of open-ended tasks with rich formats. Research Execution: Drive projects by defining key research questions and designing experiments that provide clear, actionable answers. Product Impact: Collaborate with cross-functional teams to land research breakthroughs directly into Google products and services. About you In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience: PhD or MSc in Computer Science, Machine Learning, or a related technical field. RS: PhD Experience in model alignment or post-training techniques, such as SFT, Reinforcement Learning (RL), or Reward Modeling. Experience building and maintaining large-scale data processing or distillation pipelines (e.g., using Python, Spark, or similar frameworks). In addition, the following would be an advantage: Experience with distributed training frameworks and optimizing model performance for complex, open-ended tasks. Experience working with agentic frameworks to evaluate or steer LLM behavior in interactive environments. Demonstrated ability to translate research breakthroughs into production-level features or products. A passion for building collaborative AI that excels at complex information synthesis. A proven track record of development real-world systems, research or publications in areas such as r multimodal generation, reasoning, multi-turn dialogue, agentic evaluation. The US base salary range for this full-time position is between $147,000 USD - 211,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process. Note: In the event your application is successful and an offer of employment is made to you, any offer of employment will be conditional on the results of a background check, performed by a third party acting on our behalf. For more information on how we handle your data, please see our Applicant and Candidate Privacy Policy.

Responsibilities

The role involves re-architecting how Gemini interacts, reasons, and collaborates with users over complex, multi-turn exchanges by building high-scale distillation pipelines and developing evaluation techniques. Key responsibilities include improving model quality via cutting-edge interventions, processing large-scale data, building better evaluation methods, driving research projects, and landing breakthroughs into Google products.