Job DescriptionAbout the team
Our Core AI Research team develops novel methods for enterprise agents that reason over multimodal information, use tools, take reliable action across stateful workflows, and improve through feedback. We work across LLM model post-training, agent harnesses, training environments, evaluations, ML, search and reasoning systems, partnering closely with product, engineering, infrastructure, security, and domain experts.
About the role
As a Staff Research Scientist, you will independently lead a major workstream in agent learning and recursive self-improvement. You will turn systematic failures and successful trajectories into hypotheses, experiments, training signals, and deployable improvements to model weights and/or the executable harness around the model.
This is a research role for someone who can move between scientific reasoning, training code, agent systems, and production constraints.
What you get to do in this role:- 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.
QualificationsTo be successful in this role you have: - 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 or multimodal models.
- Advanced Python and PyTorch skills, including modifying training code, data pipelines, evaluators, or research infrastructure.
- Practical depth in agentic AI, including tool use, planning, memory, retrieval, environments, or long-horizon execution.
- Experience designing decision-useful evaluations using robust datasets, trajectory analysis, graders or verifiers, and error analysis.
- Experience with distributed training, rollout, or inference and modern post-training or serving stacks.
- Strong software engineering fundamentals and evidence of research impact through publications, shipped systems, patents, benchmarks, or open source.
Preferred qualifications - Experience in multimodal, document AI, computer vision, speech/audio, or multilingual modeling.
- Experience with enterprise agents, stateful workflows, computer use, tool protocols, or simulation environments.
- Experience with synthetic data, model-generated feedback, automated experimentation, or search-based optimization.
- Experience operating distributed GPU and experiment infrastructure.
What success looks like - You establish a portfolio of reproducible improvement loops for important enterprise-agent capabilities and deliver gains that generalize across tasks, domains, or models.
- You translate multiple validated research results into production or shared-platform improvements adopted across teams, supported by clear quality, reliability, cost, and safety evidence.
- You raise organization-wide research velocity and decision quality through reusable environments, evaluation infrastructure, methodology, and cross-team technical leadership.
For positions in this location, we offer a base pay of
$231,500 - $405,100, 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 InformationWork PersonasWe 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.
