Research Scientist

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.

• Design and execute end-to-end research projects that improve long-horizon enterprise agents across planning, reasoning, memory, tool use, retrieval, computer use, multi-agent coordination, and verification.
• Research model post-training methods such as continued pretraining, supervised fine-tuning (SFT), RL, DPO/GRPO, reward modeling, and distillation.
• Research harness-level optimization across prompts and task framing, tool and schema design, skills, MCP-backed providers, subagents, context and memory management, agent-loop policy, and reliable verifiers.
• Build improvement flywheels that mine trajectories and production-safe signals, identify recurring failure modes, generate or curate data, propose interventions, and measure generalization before promotion.
• Create realistic, stateful training environments and benchmarks for enterprise workflows, with programmatic verifiers and calibrated human or model-based graders where deterministic grading is not possible.
• Run rigorous ablations and scaling experiments; reason explicitly about variance, contamination, reward hacking, distribution shift, cross-model transfer, cost, and latency.
• Develop capabilities across one or more modalities - language, documents, images/video, and speech/audio - and across multilingual or cross-lingual settings.
• Build reproducible distributed pipelines for training, rollout generation, evaluation, and inference; profile and resolve bottlenecks that only appear at scale.
• Partner with other researchers, engineering, and product teams to move validated methods into reliable enterprise systems.
• Communicate results through research reviews, technical reports, publications, patents, open-source contributions, and decision-ready recommendations.

• 10+ years of relevant AI/ML research or engineering experience, or equivalent research depth and impact; PhD or other advanced degree required.
• Track record of setting technical direction and leading multiple ambiguous, high-impact research efforts across team boundaries.
• Strong foundations in machine learning, deep learning, reinforcement learning, and experimentation, with hands-on experience training or adapting large language models.