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Mercor

Automates candidate screening and matching

Research Engineer - Real Environments

AI ResearchUpdated on Nov 10
Full-timeSan Francisco, CA, USA$160k - $500k /yr

About the Job

Requirements

  • Prior experience shipping environments, such as contributing to an open-source framework, building environments at previous companies, or working on agentic evaluations.
  • Strong full-stack engineering skills across infrastructure, application code, and analytics.
  • Curiosity about model behavior, including examining data and digging into how models work.
  • Attention to simulation details and systems-level thinking to scale quality.

Responsibilities

  • Ship models for workflow extraction, classification, and grading.
  • Engineer autonomous task-refinement processes that distill data taste into pipelines.
  • Deliver data to customers and deploy it in real engagements.
  • Help define agentic transformation for enterprises.
  • Learn enterprise workflows by building evaluations for different aspects of work.
  • Build end-to-end environments for labs and enterprises by platformizing sandbox application clones, loading real data into the sandboxes, creating prompts from real workflows, and writing verifiers using enterprise expertise and golden outputs.
  • Systematize environment production to scale throughput while maintaining high-quality worlds and verifiers.

Desired Qualifications

  • Experience with Temporal, Modal, or similar orchestration or compute services.
  • Experience with synthetic data generation for frontier models.
  • Past work auditing and scrutinizing industry-standard evaluations.

About the Company

Mercor

Automates candidate screening and matching

Mercor provides an AI-driven recruitment platform that automates key parts of hiring for both job seekers and employers. Job seekers upload their CVs and complete a 20-minute AI-led video interview, after which a proprietary language model generates detailed candidate profiles; employers receive automated resume screening, candidate matching, and initial interviews, with tailored AI interviews for specialized roles. The system uses a company-trained model to rank and recommend candidates by qualifications and predicted performance, and operates on a finder’s-fee revenue model with large-scale usage across software, operations, design, and other fields, including AI labs. Its goal is to speed up hiring while reducing bias and delivering better job-to-citizen matches.

Company size
201-500
Company stage
Series D
Total funding
$1.2B
Headquarters
Menlo Park, CA
Founded
2023

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