ServiceNow is actively hiring for 32 AI and machine learning positions across AI/ML Engineer (20), AI Product Manager (5), and AI Agent Developer (5) roles. Posted salary ranges span $158K - $420K. The median posted ceiling sits at $308K. Positions are based in Santa Clara, CA, US, London, England, UK, Chicago, IL, US. The most frequently requested skills across these postings are Python, Kubernetes, Prompt Engineering, Claude, Golang. Senior-level roles account for 78% of openings.
Skills & Technologies
Locations
Santa Clara, CA, US, London, England, UK, Chicago, IL, US, Boston, MA, US, San Diego, CA, US
Hiring by Role Category
Open Positions (showing 25 of 32)
Senior Research Engineering Scientist
Forward Deployed Solution Engineer – Applied AI FDE
Technical Product Manager, Legal AI and Platforms
Principal Machine Learning Engineer
Staff Machine Learning Engineer
Staff Machine Learning Engineer - VoIP Infrastructure
Staff AI-Native DevOps Engineer
Senior Staff AI-Native DevOps Engineer
Senior Machine Learning Engineer_Linguistics and Speech
Staff Research Scientist
Senior Staff Technical Product Manager, Conversational AI
Principal, AI Deployment Strategist, FDE
Staff Outbound Product Manager - AI Agentic Platform-Moveworks
Senior Staff Inbound Product Manager - Agentic AI Platform - Moveworks
Distinguished Product Manager, Ai Agent Products
Senior Staff Machine Learning Engineer_Voice AI Engineer
Staff Machine Learning Engineer_Voice AI Engineer
Staff Voice AI Engineer – SRE/DevOps
Senior AI Solution Architect
Sr Staff Software Engineer ( AI Agents, Observability)
Senior (DevOps) Machine Learning Engineer
Sr Staff Outbound Product Manager - AI Assistant
Machine Learning Engineer
Senior ML Engineer
What ServiceNow's hiring tells you
With 32 active AI roles spanning 5 role types, hiring at this scale signals AI is core to the business model, not a pilot. Companies in this tier typically have a named AI leader (VP AI, Head of ML), dedicated infrastructure budget, and a multi-year roadmap. Posted compensation range ($158K - $420K) suggests transparent and competitive pay practices.
The skill mix here leans toward Python in Research Engineer roles. That is a clue about what ServiceNow is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the ServiceNow interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- How is the AI org structured, and who does it report to (CTO, CEO, separate AI leader)?
- What was the most recent ML system that shipped to production, and what was the scope?
- How much of compute spend is on inference vs training, and how is that decided?
ServiceNow AI and ML Hiring
ServiceNow has 32 active AI and ML roles in our dataset. Open positions span Research Engineer, AI/ML Engineer, AI Product Manager, Research Scientist. Compensation ranges from $158K - $420K across disclosed roles. Roles are based in Santa Clara, CA, US, London, England, UK, Chicago, IL, US, Boston, MA, US.
Salary Benchmarks
The market median for AI roles is $213,480. Research Engineer roles pay a median of $252,000 across the market. AI/ML Engineer roles pay a median of $212,040 across the market. AI Product Manager roles pay a median of $220,200 across the market. Top-quartile AI compensation starts at $264,000.
Skills ServiceNow Looks For
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
AI Role Categories
Research Engineer
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Market compensation for Research Engineer roles: $252,000 median across 476 positions with disclosed pay.
AI/ML Engineer
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Market compensation for AI/ML Engineer roles: $212,040 median across 13,780 positions with disclosed pay.
AI Product Manager
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
Market compensation for AI Product Manager roles: $220,200 median across 993 positions with disclosed pay.
Research Scientist
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Market compensation for Research Scientist roles: $223,400 median across 789 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Frequently Asked Questions
Frequently Asked Questions
Related Resources
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