Applied Research Scientist at ServiceNow – Santa Clara, California

Staff Applied Research Scientist at Service Now – Santa Clara, California , United States Company Description

It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — Service Now stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work.

But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.

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(LLM’s) for Enterprise Language Generation that will power 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.

What you get to do in this role:

• 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 LLM’s 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 distilation, supervised fine-tunning and policy optimization
• Experience in using Al 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, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry.
• 6+ years of relevant experience with a Bachelor’s degree; or 4years with a Master’s degree; or a PhD with no experience; or equivalent work experience.
• Expertise in Python,OOP, Design Patterns.
• Experience in Pretraining 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.

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