Applied AI Frontiers Engineer: Data Pipelines & Models

Fdetimes; New York (NY); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Bi-annual bonus

Equity grant

Relocation bonus

Proximity bonus

Meals stipend

Equinox membership

Laundry reimbursement

Wellness reimbursement

Health/Dental/Vision

Job summary

Mercor is seeking a Software Engineer for the Applied AI – Frontier Engineering team in New York City. You will build, deploy, and operate systems bridging frontier AI research and data delivery, owning projects end-to-end from requirements gathering to production.

This high-ownership, deeply technical role requires prototyping with researchers and customers, designing scalable data pipelines, and shipping robust data tooling to support model evaluation and synthetic data generation.

Qualifications

• Backend engineering fundamentals in modern languages (Python/Go/Rust).
• Experience with model training and inference workflows.
• Strong grounding in statistics and experimental design for model performance.
• Familiarity with evaluation methods for large language models.
• Comfort shipping iteratively while navigating ambiguity.

Responsibilities

• Build and operate scalable data pipelines for post-training workflows and model evaluations.
• Design and build scalable systems for synthetic data generation and data quality; work with customers to translate requirements into solutions.
• Prototype new data types, benchmarks, and evaluation frameworks.
• Lead technical discussions with customers and frontier AI labs.
• Own projects end-to-end: requirements gathering, data creation pipelines, production improvements.

Skills

Backend engineering

ML training & inference

Statistics & experimental design

LLM evaluation methods

Ambiguity tolerance & shipping

Job description

Mercor is seeking a Software Engineer for the Applied AI – Frontier Engineering team in New York City. You will build, deploy, and operate systems bridging frontier AI research and data delivery, owning projects end-to-end from requirements gathering to production.

This high-ownership, deeply technical role requires prototyping with researchers and customers, designing scalable data pipelines, and shipping robust data tooling to support model evaluation and synthetic data generation.