Senior Research EngineerScientist

The CoreLLM team at Service Now leads groundbreaking research engineering building Service Now's signature models (NOWLLM). The effort involves all phases of Large Language Models development including data curation, training and evaluation. Our goal is to consistently enhance and improve NOWLLM models by incorporating innovative features and optimizing efficiency, establishing a differentiated advantage for applications on the Service Now Platform.

These models (NOWLLM) are consumed across various Business Units (BUs) within Service Now to power a wide range of BU-specific use cases enabling teams to solve complex problems and deliver tailored solutions that accelerate innovation and efficiency across the platform.

You will play a major part in building next generation Large Language Models (LLMs) for Enterprise Language Generation that will power the NOW platform with AI experiences in day‑to‑day work of our customers. We are just getting started with our early‑adopter customers and we need your help in building and making available an amazing range of solutions to our 9k enterprise customers around the world.

• Confronted with real‑world challenges and datasets you will need to use your AI/ML expertise and creativity to apply existing methods and develop new ones to solve these problems in a practical and scalable way.
• Research and propose appropriate models/techniques as well as implement, train and evaluate the models yourself.
• Contribute to the design, implementation and scaling of LLMs as a key AI‑first platform offering in Service Now's portfolio.
• Collaborate daily with a team of like‑minded developers, applied research scientists, product managers and quality engineers to produce quality software.
• Work with product owners to understand detailed requirements and own your code from design, implementation, testing and delivery of high‑quality & high‑impact solutions to our users.

• Expert in prompt engineering and developing LLM‑based features
• Experience with methods of training and fine‑tuning large language models such as distillation, supervised fine‑tuning and policy optimization
• Experience in using AI productivity tools such as Cursor, Windsurf, etc. is a plus or nice to have
• Experience in leveraging or critically thinking about how to integrate AI into work processes, decision‑making or problem‑solving. This may include using AI‑powered tools to automate workflows, analyze AI‑driven insights or explore AI's potential impact on the function or industry.
• 6 years of relevant experience with a Bachelors degree; or 4 years with a Masters degree; or a PhD with no experience; or equivalent work experience.
• Expertise in Python OOP Design Patterns.
• Experience in pre‑training of an LLM is preferred but not mandatory.
• Experience with instruction fine‑tuning and other fine‑tuning techniques is a must.
• Experience with reinforcement learning is preferred but not mandatory.
• Experience with various transformer architectures (auto‑regressive, sequence‑to‑sequence, etc.).
• Ability to read latest papers and experiment with the ideas.
• Good publication record in top‑tier conferences such as ICLR, NeurIPS, ICML, ACL, EMNLP, AAAI, etc.
• Communicating your research findings to both technical and non‑technical stakeholders and working to ensure that the benefits and limitations of LLMs are clearly understood across the organization.

For positions in this location we offer a base pay of $182,300 - $310,000 plus equity (when applicable), variable/incentive compensation and benefits. Compensation is based on the geographic location in which the role is located and is subject to change based on work location. We also offer health plans, flexible spending accounts, a 401(k) plan with company match, ESPP, matching donations, flexible time away plan and family leave programs.

We approach our distributed world of work with flexibility and trust. Work personas (flexible remote or required in office) are categories that are assigned to Service Now employees depending on the nature of their work and their assigned work location. Learn more here (link removed).

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