• As a Principal Decision Scientist, Machine Learning Engineer you will: • Define high-level business objectives directly with clients, then develop and execute the project plan to meet those objectives • Proactively research and apply knowledge within the data science space to deliver best-in-class solutions • Lead both small and large teams over the entire data science lifecycle – from problem definition to model automation and deployment • Provide technical leadership to guide development work across teams while also owning and delivering specific technical components yourself • Manage all aspects of client relationships and create value-driving initiatives for the company • Design and develop feature engineering pipelines, build ML & AI infrastructure, deploy models, and orchestrate advanced analytical insights • Write code in SQL, Python, and Spark following software engineering best practices

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 - ServiceNow 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. Job Description What you get to do in this role: The Virtual Agent Science team is at the forefront of conversational AI, specializing in delivering intelligent, human-like interactions through virtual agents. Our mission is to enhance customer experiences by advancing the state of the art in search and conversational science. We tackle the entire lifecycle of conversational AI-from understanding user intent and retrieving relevant documents to crafting natural and context-aware personalized responses. We collaborate closely with product teams to translate business requirements into robust, measurable solutions. Our work includes developing and refining benchmarks for conversational systems, leveraging user feedback to continuously improve performance, and innovating in areas like semantic search, language modeling, and dialogue management. By combining cutting-edge research with practical applications, we ensure our virtual agents are capable of retrieving the most relevant information, responding effectively to user queries, and gracefully handling scenarios where clarification or "no answer found" responses are required. You will play a major part in building AI and Machine Learning (ML) solutions that transform the user experience and workflow efficiency of enterprise services. Traditional analytical tools tend to require a technically knowledgeable user to produce even simple results. We are taking a completely fresh approach with the expectation that any user, regardless of technical knowledge, can use the AI/ML solutions we develop to operate the services in the enterprise setting in a thoughtful and scalable manner. 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 5k+ enterprise customers around the world. What you'll do in the role: - Build the best cloud-based AI/ML solutions to power intelligent enterprise services. - Collaborate daily with a team of like-minded developers, 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 solutions to our users. - Creating and maintaining large-scale data processing pipelines that ensure that data is processed accurately, efficiently, and securely Qualifications To be successful in this role you have: - Proficient 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. - 10+ years of related experience with a bachelor's degree; or 8 years and a master's degree; or a PhD with 5 years' experience; or equivalent work experience - 4+ years of management and/or technical lead experience, proficiency in providing feedback to work group strategies, policies and plans - Experience building new products that use challenging algorithms in hands of customers. - Proficient with methods of prompt tuning LLMs. Good to have some awareness of fine-tuning large language models, such as distillation, supervised fine-tuning and policy optimization. - Knowledge of core AI/ML techniques and algorithms - Expertise in coding efficient, object-oriented, modularized and quality software. Knowledge of unit testing, profiling, and code tuning. For positions in this location, we offer a base pay of $197,800 - $346,200, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location. Additional Information Work Personas 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 ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2025 Fortune Media IP Limited. All rights reserved. Used under license.

• Design, implement, and maintain CI/CD pipelines for ML models and data workflows using AWS-native services and infrastructure-as-code. • Operationalize models built on SageMaker, Bedrock, and Snowflake Cortex, including feature pipelines, training, batch/real-time inference, and monitoring. • Build and manage data pipelines and feature stores using services such as AWS Glue, Lambda, Step Functions, and Snowflake. • Implement observability for ML systems (logging, metrics, tracing, drift/quality monitoring) and establish SLOs/SLAs for production ML services. • Automate environment provisioning, configuration, and dependency management across dev, test, and production. • Partner with security and compliance teams to ensure ML workloads meet healthcare, privacy, and regulatory standards (e.g., HIPAA). • Collaborate with ML engineers and data scientists to productionize notebooks and prototypes into robust, maintainable services. • Contribute to best practices, standards, and documentation for ML platform and operations across the organization.

• Support for clusters (*GPU*/*TPU*), containerization, and orchestration (*Docker*, *Kubernetes*); • Audit and optimization of infrastructure and costs (batch vs. real-time inference, distinguishing hardware versus code issues); • Setting up monitoring, alerting, and managing hardware resources between users; • Deployment and support of *ML* services (*MLflow*, *Airflow*, *CVAT*, *Doccano*) in production; • Configuring CI/CD for *ML* pipelines and managing the complete *ML* lifecycle (training -> serving).