Tech Lead, Applied AI - Search
Mercor · San Francisco
About this role
## 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, and we’re now bringing those capabilities to enterprises across industries. ## Role: Engineering Lead (Applied AI – Search) Mercor is hiring Engineering Leads to lead teams within our **Applied AI** organization. Applied AI builds the systems that directly determine how fast frontier models improve—such as: - Evaluation infrastructure - Rubric and reward-model pipelines - Annotation products - Emerging multimodal capabilities (e.g., screen-capture-grounded agent training) This is not generic ML infrastructure—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 partnerships with leading AI labs and enterprises, and contribute hands-on to improving frontier model quality through data, evaluation, and systems—focused on areas the field agrees matter most right now: - RL with verifiable rewards - Rubric-based reward modeling for subjective and agentic domains - Pipelines that turn raw human demonstrations into training-ready environments ## What You’ll Work On - **Build and scale teams**: coach and develop engineers; establish ownership, culture, and execution standards; define hiring processes and engineering practices. - **Drive Applied AI systems and outcomes**: - Own the methodology behind evaluation systems (e.g., rubric design, LLM-judge calibration, reward-hacking mitigation) - Lead pipelines converting AI-moderated interviews and human demonstrations into skills, rubrics, and post-training data at scale - Improve data quality, operational efficiency, system stability, and scalability across human-in-the-loop workflows - Push capabilities toward greater **verifiability**, including subjective and agentic work where rubric quality and domain expertise are bottlenecks - **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 ambiguous, research-adjacent problems - Translate emerging research (e.g., RLVR, rubric-as-reward, agent grounding from human demonstrations) into engineering roadmaps - **Operate across a broad scope**: - Partner with product, research, operations, and external AI labs on custom evaluation and training-data engagements - Move model quality metrics directly through systems, data insights, and lab-specific partnerships - **Shape the organization**: - Introduce lightweight processes as the organization scales - Hire and develop strong engineering talent - Help define what engineering management looks like at Mercor ## What We’re Looking For - **6–10 years** in engineering; **2–3+ years** leading teams - Strong background building **scalable systems** - Comfort with core **ML/LLM concepts**, including post-training, evaluat
Listing freshness
CronJobs last confirmed this listing 58m ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.