# Tech Lead, Applied AI - Search at Mercor

Mercor is hiring a Tech Lead, Applied AI - Search in San Francisco, United States. It pays $350k-$600k a year and Level rates it Works on AI ●●●○; you can [apply on Mercor](https://jobsbylevel.com/go/34327540-e6c8-4144-9bc1-d9f0de60277a?utm_medium=ai-agent).

AI Level 3, AI centrality 67 out of 100. San Francisco.

## Details

- Company: [Mercor](https://jobsbylevel.com/companies/mercor)
- AI level: AI Level 3 (score 67 out of 100)
- Location: San Francisco
- Salary: $350k-$600k
- Posted: August 17, 2026
- Apply: https://jobsbylevel.com/go/34327540-e6c8-4144-9bc1-d9f0de60277a?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=tech-lead-applied-ai-search-at-mercor-6c39dd

## Prepare for this job

The job page lists 3 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/tech-lead-applied-ai-search-at-mercor-6c39dd

## Description

About Mercor Mercor’s mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise. We built our training and evaluation infrastructure alongside the world’s leading AI labs. Now, we’re bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows. Our work sits at the intersection of frontier AI, human expertise, and real-world application. We’re solving problems few companies have encountered, with the opportunity to influence what the world’s most advanced AI systems become capable of doing. We’re looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You’ll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence. Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London. We're hiring Engineering Leads to lead teams within our Applied AI organization. Applied AI builds the systems that directly determine how fast frontier models improve: evaluation infrastructure, rubric and reward-model pipelines, annotation products, and emerging multimodal capabilities like screen-capture-grounded agent training. This isn't generic ML infra — it's the layer where human expertise gets translated into signal that labs can actually train on. You'll lead a team of engineers, partner closely with product, research, and operations, and help define how Mercor stays at the frontier as the industry shifts from static data labeling to dynamic, verifiable training environments. You'll also build direct partnerships with leading AI labs and enterprises, and contribute hands-on to improving frontier model quality through data, evaluation, and systems — work that sits squarely in the areas the field agrees matter most right now: RL with verifiable rewards, rubric-based reward modeling for subjective and agentic domains, and the pipelines that turn raw human demonstrations into training-ready environments. This role requires high ownership and abstraction: setting direction, driving outcomes, and staying hands-on while leading. What You'll Work On Build and scale teams Coach and develop high-potential engineers Establish strong ownership, culture, and execution standards Shape the team: define hiring processes, establish engineering practices, and scale a high-performing team from the ground up Drive Applied AI systems and outcomes Own the methodology behind evaluation systems that benchmark and improve model performance — from rubric design and LLM-judge calibration to reward-hacking mitigation Lead development of pipelines that convert AI-moderated interviews and human demonstrations into skills, rubrics, and post-training data at scale Drive improvements in data quality, operational efficiency, system stability, and scalability across human-in-the-loop workflows Push capabilities up the verifiability spectrum — from cleanly-graded tasks toward the harder, subjective, and agentic work where domain expertise and rubric quality become the bottleneck Own technical direction Lead system design for complex AI systems, including multimodal and agentic evaluation pipelines Stay close to the technical work and guide engineers through genuinely ambiguous, research-adjacent problems Translate

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Source: https://jobsbylevel.com/jobs/tech-lead-applied-ai-search-at-mercor-6c39dd

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