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About This Role
About Mercor
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Mercor's mission is to organize human intelligence to power the AI economy. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development. Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $3 million a day.
Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast\-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in\-person five days a week in our San Francisco, NYC, or London offices.
About the Role
Within Mercor, our Enterprise AI organization builds the infrastructure enterprises need to deploy continuously improving production AI systems. Rather than relying on hand\-written prompts and manual workflows, we help organizations translate how their best people work into agent behavior, evaluate outputs against measurable quality standards, and create feedback loops that make agents smarter over time.
Our platform combines organizational context, evaluation systems, and quality guardrails into a stack that powers accelerated AI deployment in weeks instead of months. Proven at scale with the world’s leading frontier AI labs, our technology now powers AI deployments across Fortune 500 organizations.
This full\-time leadership role sits within Applied AI (the Forward Deployed Engineering team of our Enterprise AI organization) and operates separately from Mercor's expert network.
We're hiring Applied AI Leads to build and scale the technical delivery team responsible for deploying Mercor's Enterprise AI platform for enterprise customers.
This is a highly customer\-facing engineering leadership role that is tightly integrated with our Sales/GTM and delivery teams. You’ll lead engineers in the end\-to\-end technical delivery of production\-grade AI systems, working hand\-in\-hand with customer stakeholders to solve technically complex business problems and contributing to the overall commercial relationship with the customer.
Success is measured by customer outcomes and overall commercial impact, not individual code output. You'll own technical delivery, coach engineers, remove blockers, and ensure deployments are reliable, scalable, and customer\-ready. You have a deep understanding of customer workflows, use case requirements, and acceptance criteria. While you’re expected to be technically credible and capable of hands\-on contribution, your primary responsibility is enabling your engineering team to execute.
What You'll Do
- Partner directly with customers to align technical requirements with business objectives, acting as the critical bridge between engineering and Sales, GTM, and delivery
- Co\-lead a Technical team with an Operations counterpart to deliver scalable, production\-ready AI systems for enterprise customers
- Translate ambiguous workflows into scalable AI\-powered solutions, integrating across enterprise systems, APIs, internal tools, and data sources
- Guide the deployment of AI workflows, agent orchestration, evaluation systems, and continual learning feedback loops
- Hire, mentor, and develop exceptional engineers while establishing rigorous engineering and technical delivery practices as the organization scales
- Work closely with Sales/GTM, Product, Research, and Platform Engineering to bring new AI capabilities into production
What We're Looking For
- 5\+ years building production software systems, with strong software engineering fundamentals in Python, Go, Java, Rust, or similar languages
- 2\+ years directly managing technical teams
- Proven track record leading customer\-facing technical delivery for enterprise software, AI products, or developer platforms
- Deep experience designing designing distributed systems and cloud\-native applications
- Highly comfortable communicating complex technical concepts to both engineers and executive\-level customer stakeholders
- Demonstrated ability to lead through ambiguity, coach others, make decisive technical calls, and maintain high execution velocity in unstructured environments
Nice to Haves
- AI agents or agent orchestration
- LLM applications
- Evaluation systems or benchmarking
- Enterprise integrations
- Workflow automation
- Production ML systems
Who Thrives Here
You enjoy solving ambiguous technical problems with customers, building high\-performing teams, and shipping production systems that create measurable business value.
You are a technical delivery leader first, and an individual contributor second. Your instinct is to remove blockers, build deep trust with enterprise stakeholders, and partner closely with sales to ensure mutual success.
Why Join Mercor
- Build production AI systems deployed for some of the world's largest enterprises
- Help define how organizations adopt agentic AI at scale
- Lead a high\-impact, forward\-deployed engineering organization from its earliest stages
- Work alongside elite engineers, researchers, and operators at the forefront of enterprise
Benefits
- Participation in the Enterprise revenue sharing pool
- Generous equity grant vested over 4 years
- Up to $15k Relocation bonus
- $10K housing bonus (if you live within 0\.5 miles of our office)
- $1\.5K monthly stipend for meals
- Free Equinox membership
- $200 monthly laundry reimbursement
- $200 monthly personal wellness reimbursement
- Health, Dental, Vision insurance
Compensation Range: $300K \- $520K
Salary Context
This $300K-$520K range is above the 75th percentile 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 MERCOR, 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 ($410K) sits 87% above the category median. Disclosed range: $300K to $520K.
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.
MERCOR AI Hiring
MERCOR has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $520K - $520K.
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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