Interested in this AI/ML Engineer role at CURSOR?
Apply Now →Skills & Technologies
About This Role
Marketing · Full\-time · San Francisco
Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth\-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth\-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
### About the role
We're looking for an integrated campaigns leader to build Cursor's integrated marketing function from scratch — the programs, content, and channels that drive enterprise awareness, generate pipeline, and move executive buyers through a complex sales cycle.
This is a high\-ownership role that sits at the intersection of strategy and execution. In partnership with existing growth pods, you'll define Cursor's enterprise marketing plays, run multi\-channel demand generation programs, own the account based marketing strategy, and bring customer stories to life in ways that resonate with technical buyers and economic decision\-makers alike. You'll also hire and grow the team that scales all of it.
### What you'll do
- Own Cursor's integrated campaigns strategy for new Enterprise accounts — partnering across Marketing to coordinate narratives, content, paid, email, and lifecycle channels into cohesive programs that drive measurable pipeline
- Create and own the strategy for a full\-funnel ABM engine with clear pipeline attribution and ROI reporting
- Build and operate marketing programs that attract net new enterprise buyers from first touch through closed\-won
- Develop and steward Cursor's enterprise content narrative — the point of view, themes, and messaging frameworks that inform everything from a blog post to a board\-level deck
- Establish an editorial calendar and content production system – focused on customer storytelling: original research, executive bylines, industry reports, and editorial content aimed at engineering leaders, CTOs, and technical decision\-makers – that scales output without sacrificing quality — including partnering internally, freelancer networks, agency relationships, and AI\-assisted workflows
- Own the customer storytelling program: identify, develop, and amplify customer case studies, video testimonials, and reference stories that prove enterprise value across key verticals and use cases
- Partner with Product Marketing to translate product launches and positioning into campaign\-ready content and activation plans
- Develop the content and messaging strategy for webinars, virtual events, and digital experiences that drive top\-of\-funnel awareness and mid\-funnel conversion
- Define the metrics that matter and build dashboards that keep the team accountable
- Collaborate with Sales and Revenue Operations to ensure campaign\-sourced leads are followed up effectively and that content is serving real buying conversations
- Build, hire, and develop a team of content strategists, campaign managers, and customer marketers who can operate at both the strategic and executional level
### You may be a fit if
- You have 8\+ years of B2B marketing experience, with meaningful time spent in content, integrated campaigns, or demand generation at a high\-growth enterprise software or developer tools company
- You've built and led a team — you know how to hire great marketers, set a high bar for quality, and create a culture where creative work gets done fast
- You have a strong editorial instinct: you can spot a compelling enterprise story, shape a point of view, and hold the line on quality when the calendar is full
- You've run full\-funnel demand gen programs with real accountability to pipeline — you understand attribution, know your way around a CRM, and can explain why a campaign worked or didn't
- You've built lifecycle and nurture programs using marketing automation platforms (HubSpot, Marketo, or similar) and understand how to design journeys that actually move buyers
- You have experience developing thought leadership content — executive ghostwriting, original research, industry reports — that earns coverage and builds category credibility
- You've owned customer storytelling end\-to\-end: identifying reference customers, managing the content development process, and activating stories across channels
- You're equally comfortable in a strategy doc and a content brief — you can write well yourself and can raise the quality of everyone around you
- You understand how to market to technical buyers: you don't dumb things down, you translate complexity into clarity
- You move fast, communicate proactively, and operate with a high degree of ownership in a flat, high\-trust environment
- You are willing to go above and beyond when it matters
Role Details
About This Role
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.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At CURSOR, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
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.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
CURSOR AI Hiring
CURSOR has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, Remote, US.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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 (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.