Interested in this AI/ML Engineer role at monday.com?
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About This Role
monday.com is the AI work platform powering the most ambitious teams. 250,000\+ customers across departments use us to bring people, workflows, and AI agents together on one flexible platform where AI doesn't just assist, it executes. We move fast, build things that matter, and foster an ownership\-driven culture where you're empowered to shape how organizations work and outpace their competition.
Joining our team means you're architecting how organizations run in the AI era. You'll turn AI into high\-value solutions for 250,000\+ customers including 60% of the Fortune 500\. You’ll act as a trusted advisor backed by a strong GTM engine and over $1B in ARR. Using our foundational platform and advanced agents, you'll help the world's largest brands automate and scale their impact end\-to\-end.
We're looking for a technically elite monday.com expert to sit at the intersection of AI product and field sales. You'll be the person who turns AI strategy into working demos, templates, and agentic workflows that our NAM sales teams use every day to win deals. You will play a critical role in helping building the infrastructure that makes every rep in NAM better at AI selling
About The Role
This is a unique, dual\-impact role. Externally, you'll be the person who turns AI strategy into working demos, templates, and agentic workflows that our NAM sales teams use every day to win enterprise deals. Internally, you will partner directly with AI GTM Leadership to architect and build custom AI\-powered operational workflows that transform how our own revenue organization functions.
You will play a critical role in building the infrastructure that makes every rep in NAM better at AI selling, while simultaneously engineering the internal engines that power our AI GTM strategy at scale
What You'll Do
- Build and maintain AI\-powered monday.com templates across industries and use cases
- Design and deploy agentic workflow demos for enterprise sales cycles
- Partner with AEs and SEs on AI qualification strategies, technical discovery, and ROI narratives
- Join strategic customer calls as the AI technical expert, including live builds and workflow demonstrations
- Own and continuously evolve the AI demo environment to ensure it remains current, scalable, and field\-ready
- Translate new AI product capabilities into compelling field\-facing solutions in partnership with Product and Marketing
- Architect and build internal AI GTM workflows and operational solutions
- Design automations and AI\-driven processes that solve complex operational challenges (e.g., pipeline reviews, win/loss analysis, CI workflows)
- Build integrations across the GTM tech stack to enable seamless system connectivity and data flow
- Partner closely with RevAI, RevOps, BI, IT, and internal AI innovation teams to align technical solutions with business needs
Requirements
- 3\+ years of relevant experience in solutions architecture, technical GTM, or workflow automation, with deep hands\-on expertise in monday.com \- including boards, automations, integrations, and AI capabilities
- Experience designing AI\-powered or agentic workflows
- Strong technical architecture and workflow automation capabilities
- Experience with APIs, MCP integrations, and automation platforms such as n8n, Make, or Zapier
- Ability to translate technical complexity into clear business value
- Comfort operating in enterprise sales environments and customer\-facing engagements
- Strong cross\-functional collaboration and communication skills
- A highly proactive builder mentality with a bias toward experimentation and execution
- Deep curiosity about AI, LLMs, and emerging agentic frameworks
What monday.com can offer you:
- Opportunity to join an innovative, proven company with big ambitions, competitive salary and benefits, bonus potential, and some roles are eligible to take part in the company equity incentive program
- A team that values transparency and collaboration while having fun while we work
- Monthly stipends for food, wellness, and commuter/remote work
- Fully dedicated learning and development team that provides opportunities for employees to grow, gain new skills, master AI tools, and participate in workshops
- Award winning work environment \- named a "Best Place to Work" by Built In as well as "Great Place To Work" certified.
- We foster diversity, inclusion, and belonging through our Employee Resource Groups in addition to providing access to resources and education to support our team, facilitate conversations, and encourage understanding
- A global work environment with employees in New York, Tel Aviv, London, Sydney, São Paulo, Tokyo, and more
*Please note that this role is on a hybrid model of 3x per week in our NYC office.*
***Visa sponsorship for this role is currently not available.*
*For New York City\-based hires only: Compensation Range: $99,000\-$129,000 base salary, subject to standard withholding and applicable taxes. In addition to base salary, the role includes opportunity to receive and/or earn a discretionary bonus and/or equity based on Company’s plans and in accordance with Company’s policies. Compensation finally awarded to the candidate will be commensurate with the candidate’s skills and experience. Compensation ranges for candidates in locations outside of New York City may differ based on the cost of labor and such additional factors for such other locations.*
***monday.com* *is proud to be an equal\-opportunity employer. We hire talented individuals, regardless of gender, race, ethnicity, ancestry, age, disability, sexual orientation, gender identity or expression, military or veteran status, cultural background, religious beliefs, or any other characteristic protected by federal, state, or local laws*
*\#LI\-Hybrid*
Compensation Range: $115K \- $150K
Salary Context
This $99K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At monday.com, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($124K) sits 43% below the category median. Disclosed range: $99K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
monday.com AI Hiring
monday.com has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $150K - $200K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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