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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 $2 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
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Studio is one of Mercor's highest\-priority initiatives. It is the internal platform that powers every workflow our teams use to generate, manage, quality\-review, and deliver labeled data to the world's leading AI labs — and its quality directly shapes the frontier models that billions of people interact with.
As a Fullstack Software Engineer on the Applied AI team, you'll own Studio end\-to\-end: the React frontend used daily by thousands of expert contributors and internal Strategic Project Leads, and the backend services powering data pipelines and delivery of outputs to customers. You'll also build and maintain the foundational layer that Mercor's Leverage Engineering team depends on to rapidly deploy new data types and task formats across the company. Four distinct stakeholders — AI labs, SPLs, expert contributors, and Leverage Engineering — rely on what you build.
This is a role for someone who cares equally about engineering craft and product impact: shipping scalable, reliable systems and intuitive interfaces that make everyone who touches Studio faster and more effective.
You Will
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- Own the Studio platform across the full stack — from contributor\-facing and operator\-facing React interfaces to the backend APIs and pipelines driving data ingestion, QA review, and AI lab delivery.
- Lead engineering best practices across the team, setting the bar for frontend architecture, component design, and performance, and establishing patterns that others can build on reliably.
- Build the foundational layer that Leverage Engineering and other teams depend on to support new data types, task formats, and customer configurations quickly and without rework.
- Ship high\-leverage product features that improve throughput and quality across our core data generation and delivery pipelines.
- Translate ambiguous requirements into well\-scoped technical solutions through close collaboration with Applied AI researchers, product, design, and operations.
What We're Looking For
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- 5\+ years of fullstack engineering experience building and shipping production software.
- Deep proficiency in JavaScript/TypeScript (React \+ Next.js) and a strong grasp of frontend architecture, state management, and design patterns.
- Solid backend experience in Python and/or Node.js — experience designing and building relational databases, REST APIs, and async task infrastructure.
- Strong design sense and empathy for end\-users; you hold a high bar for usability.
- Experience with complex, high\-throughput systems — pipelines, large data volumes, or multi\-tenant architectures.
- High ownership mindset; able to navigate ambiguity and drive projects from design through delivery.
- Strong communication skills and ability to articulate technical and product tradeoffs clearly.
Nice\-to\-Haves
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- Experience building tooling for data labeling, annotation, or human\-in\-the\-loop AI workflows.
- Background in design systems, developer\-facing APIs, or internal platform engineering.
- Exposure to AI tools, LLM\-powered products, or real\-time data and visualization.
- Experience at top\-tier tech companies or fast\-scaling startups.
Why Mercor
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- Impact: Your work powers how the world's leading AI labs train and test their models — Studio is directly upstream of frontier AI development.
- Learning: Get early insights into frontier model capabilities and the data workflows that shape them, months before they reach the market.
- Growth: Work across the full stack on one of the company's top initiatives, with fast paths to ownership and technical leadership.
Benefits
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- Bi\-annual performance bonus structure
- Generous equity grant vested over 4 years
- Up to $15k Relocation bonus
- $10K proximity 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: $130K \- $500K
Salary Context
This $130K-$500K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $191K across 189 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,739 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At MERCOR, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $234,620 based on 682 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($315K) sits 34% above the category median. Disclosed range: $130K to $500K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
MERCOR AI Hiring
MERCOR has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span New York, NY, US, San Francisco, CA, US. Compensation range: $500K - $500K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,739 open positions tracked in our dataset. By seniority: 115 entry-level, 1,764 mid-level, 1,444 senior, and 416 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (597 positions). The remaining 3,119 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,739 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,650), Data Scientist (271), AI Software Engineer (252). 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 (115) are outnumbered by mid-level (1,764) and senior (1,444) 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 416 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (597 positions), with 3,119 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,929 postings), Aws (1,185 postings), Azure (869 postings), Rag (866 postings), Gcp (726 postings), Prompt Engineering (578 postings), Pytorch (575 postings), Claude (547 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.
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