The New York market for LLM Engineer positions reflects New York's finance-driven AI market, where Wall Street and fintech firms compete for talent. Salaries here typically run 15-30% above national averages, though higher cost of living offsets some of that premium. Understanding local compensation benchmarks helps you negotiate effectively and evaluate whether an offer is competitive for this specific market.
Top Paying Companies
Frequently Asked Questions
What is the average LLM Engineer salary in New York?
Based on 12 job postings with disclosed compensation, LLM Engineer roles in New York pay between $177K and $244K base salary. This 38% spread reflects differences in company stage, required skills, and specific responsibilities. The wide spread between minimum and maximum reflects significant variation across company stages, from well-funded startups offering equity-heavy packages to established enterprises with higher base pay. Your negotiating position also depends heavily on specialized skills like LLM fine-tuning, RAG architecture, or production ML deployment experience.
Why is the LLM Engineer salary range so wide?
The 38% salary spread reflects real market variation. Key factors include: (1) Company stage - startups often pay less base but offer equity; (2) Specific skills - expertise in LangChain, RAG, or fine-tuning commands premiums; (3) Industry - fintech and healthtech AI roles pay 15-25% above average; (4) Scope - building production systems vs research roles have different compensation. The wide spread between minimum and maximum reflects significant variation across company stages, from well-funded startups offering equity-heavy packages to established enterprises with higher base pay. Your negotiating position also depends heavily on specialized skills like LLM fine-tuning, RAG architecture, or production ML deployment experience.
What skills increase LLM Engineer salary?
Skills that command higher LLM Engineer salaries include: LangChain/LlamaIndex expertise (+10-15%), production RAG systems experience (+15-20%), fine-tuning experience (+10-20%), MLOps/deployment skills (+10-15%), and domain expertise in high-paying industries like finance or healthcare. Multiple LLM platform experience (OpenAI + Claude + open-source) also adds value. The wide spread between minimum and maximum reflects significant variation across company stages, from well-funded startups offering equity-heavy packages to established enterprises with higher base pay. Your negotiating position also depends heavily on specialized skills like LLM fine-tuning, RAG architecture, or production ML deployment experience.
How accurate is this AI salary data?
Our data comes from 12 actual job postings with disclosed compensation ranges, not self-reported surveys. We track AI, ML, and prompt engineering roles weekly. Limitations: not all companies disclose salary ranges, and posted ranges may differ from final negotiated offers. The wide spread between minimum and maximum reflects significant variation across company stages, from well-funded startups offering equity-heavy packages to established enterprises with higher base pay. Your negotiating position also depends heavily on specialized skills like LLM fine-tuning, RAG architecture, or production ML deployment experience.
Related Salary Data
Methodology
Salary data is collected from job postings on Indeed and company career pages. Only jobs with disclosed compensation are included. Data is updated weekly.
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About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
We're tracking 1,737 open AI roles right now.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
AI Hiring Overview
The AI job market has 1,737 open positions tracked in our dataset. By seniority: 10 entry-level, 906 mid-level, 751 senior, and 70 leadership roles (Director, VP, C-Level). Remote roles make up 21% of the market (360 positions). The remaining 1,377 roles require on-site or hybrid attendance.
The market median for AI roles is $215,400. Top-quartile compensation starts at $258,000. The 90th percentile reaches $294,700. Highest-paying categories: Research Engineer ($260,000 median, 254 roles); MLOps Engineer ($217,200 median, 13 roles); Data Scientist ($215,300 median, 82 roles).
Career Path
Common paths into roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
Skills in Demand for This Role
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
The AI Job Market Today
The AI job market spans 1,737 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (728), Research Scientist (251), Data Scientist (168). 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 (10) are outnumbered by mid-level (906) and senior (751) 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 70 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 21% of all AI roles (360 positions), with 1,377 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 $215,400. Top-quartile roles start at $258,000, and the 90th percentile reaches $294,700. 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. Research Engineer roles lead at $260,000 median, while AI Product Manager roles sit at $190,000. 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: RAG (1,290 postings), Python (1,220 postings), AWS (1,024 postings), Rust (763 postings), AI Agents (565 postings), Azure (502 postings), GCP (408 postings), Prompt Engineering (365 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.