Senior Engineering Manager, Agentic & Generative AI Benchmarking and Evaluations

ServiceNow•Santa Clara, CA

3d•$201,300 - $352,300•Flexible

About The Position

The Advanced Technology Group (ATG) at ServiceNow is a customer-focused innovation group building intelligent software and smart user experiences using existing and latest advanced technologies to enable end-to-end, industry-leading work experiences for customers. We are a group of researchers, applied scientists, engineers, and product managers with a dual mission. We build and evolve the AI platform, and partner with teams to build products and end-end AI-powered work experiences. In equal measure, we lay the foundations, research, experiment, and de-risk AI technologies that unlock new work experiences in the future. We are seeking an exceptional, data-driven Senior Engineering Manager, Agentic & GenAI Benchmarking and Evaluations to establish and lead AI evaluation practices for both ServiceNow and our customers. As ServiceNow shifts enterprise workflows from simple generation to complex, autonomous agents, ensuring system reliability, safety, and accuracy is paramount. In this role, you will lead a specialized team of AI evaluation engineers and data scientists. Your team will build the infrastructure, rigorous validation frameworks, and benchmarks that quantify the performance of Now Assist agentic workflows across multi-step orchestration, tool-calling, and enterprise-grounded reasoning. You will bridge the gap between frontier AI research and hard production metrics, directly impacting the trust and adoption of autonomous workflows for millions of enterprise users.

Requirements

8+ years of professional software engineering or machine learning experience, including 3+ years managing or technically leading high-performing AI/ML teams.
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.
Strong foundational knowledge of frontier AI SDKs and deep experience deploying or testing agentic/probabilistic software architectures (multi-agent orchestration, tool execution, and probabilistic feature deployment).
Demonstrated experience implementing rigorous AI metrics (e.g., ROUGE, BLEU, G-Eval, LLM-as-a-judge patterns, and custom deterministic evaluation code) at an enterprise scale.
Proficiency in Python and familiarity with data analytics infrastructures (SQL, Pandas, NumPy) alongside standard MLOps tracking platforms.
Experience with complex knowledge infrastructure, SaaS platform architectures, or relational datasets (e.g., Knowledge Graphs, CMDBs).
Ability to translate deeply technical evaluation data into executive-level risk assessments, ROI summaries, and strategic roadmap recommendations.
Bachelor’s or higher degree in Computer Science, Data Science, Machine Learning, or a highly quantitative field (Master's or Ph.D. is a plus).

Responsibilities

Build the Evaluation Infrastructure: Design, own, and scale automated testing and evaluation harnesses (unit evals, integration evals, and production drift monitors) to measure agent quality and eliminate regressions.
Define Enterprise AI Benchmarks: Create standard, repeatable evaluation frameworks tailored to complex business workflows—assessing multi-agent orchestration, intent routing, multi-step planning loops, and long-term memory accuracy.
Validate Grounding & RAG Pipelines: Partner with search and data fabric teams to systematically evaluate Retrieval-Augmented Generation (RAG) pipelines, hybrid search, and semantic re-ranking systems.
Model Selection Optimization: Rigorously benchmark frontier LLMs (e.g., OpenAI, Anthropic, Google, and proprietary ServiceNow models) to evaluate trade-offs across execution capabilities, latency, context-window efficiency, and inference costs.
Lead a High-Performing Team: Recruit, mentor, and foster an AI-native engineering team, driving engineering best practices, prompt-infrastructure stability, and production-grade rigor.
Cross-Functional Leadership: Collaborate with Core Product, Machine Learning Platforms, and Engineering leads to translate baseline performance statistics into actionable product improvements and model fine-tuning targets.