Research Engineer Intern - Environments, Data and Post-Training

mercor.com

San Francisco (CA)

In loco

USD 180.000 - 280.000

Tempo pieno

10 giorni fa

Una candidatura completa in un minuto

Curriculum e lettera di presentazione personalizzati, pronti da inviare.

Supera i filtri ATS

Vantaggi offerti da questo lavoro

Health benefits

Overtime pay

Meals stipend

Relocation assistance

Housing support

Equinox membership

Laundry reimbursement

Wellness reimbursement

Descrizione del lavoro

Mercor is seeking a Research Engineer to join our in-person office-based teams in San Francisco, NYC, or London. You will work at the intersection of engineering and applied AI research, contributing to post-training, RLVR, synthetic data generation, and large-scale evaluation workflows that impact frontier language models.

You will help train large language models to master tool use, agentic behavior, and real-world reasoning in production environments, shaping rewards, running experiments, and

Competenze

Strong applied research background in ML/AI.
Solid coding proficiency with ML models.
Strong understanding of data structures and algorithms.
Experience with backend systems and scalable software.
Familiarity with APIs and SQL/NoSQL databases.
Experience with cloud platforms.
Ability to reason about model behavior and experiments.

Mansioni

Work on post-training and RLVR pipelines to understand data, rewards, and training strategies impact on model performance.
Design and run reward-shaping experiments to improve LLM tool-use and reasoning.
Quantify data usability, quality, and performance uplift on benchmarks.
Build and maintain data generation and augmentation pipelines scalable to training needs.
Create rubrics, evaluators, and scoring frameworks for training and evaluation decisions.
Build and operate LLM evaluation systems, benchmarks, and metrics at scale.
Collaborate with AI researchers, applied AI teams, and data-producing experts.
Operate in a fast-paced research environment with rapid iteration and high ownership.

Conoscenze

Applied research

Coding proficiency

Data structures

Algorithms

Backend systems

APIs

SQL/NoSQL

Cloud platforms

Model evaluation

Experimental design

Descrizione del lavoro

ABOUT MERCOR

Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.

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

As a Research Engineer at Mercor, you’ll work at the intersection of engineering and applied AI research. You’ll contribute directly to post-training and RLVR, synthetic data generation, and large-scale evaluation workflows that meaningfully impact frontier language models.

Your work will be used to train large language models to master tool use, agentic behavior, and real-world reasoning in real-world production environments. You’ll shape rewards, run post-training experiments, and build scalable systems that improve model performance. You’ll help design and evaluate datasets, create scalable data augmentation pipelines, and build rubrics and evaluators that push the boundaries of what LLMs can learn.

WHAT YOU’LL DO

Work on post-training and RLVR pipelines to understand how datasets, rewards, and training strategies impact model performance.
Design and run reward-shaping experiments and algorithmic improvements (e.g., GRPO, DAPO) to improve LLM tool-use, agentic behavior, and real-world reasoning.
Quantify data usability, quality, and performance uplift on key benchmarks.
Build and maintain data generation and augmentation pipelines that scale with training needs.
Create and refine rubrics, evaluators, and scoring frameworks that guide training and evaluation decisions.
Build and operate LLM evaluation systems, benchmarks, and metrics at scale.
Collaborate closely with AI researchers, applied AI teams, and experts producing training data.
Operate in a fast-paced, experimental research environment with rapid iteration cycles and high ownership.

WHAT WE’RE LOOKING FOR

Strong applied research background, with a focus on post-training and/or model evaluation.
Strong coding proficiency and hands-on experience working with machine learning models.
Strong understanding of data structures, algorithms, backend systems, and core engineering fundamentals.
Familiarity with APIs, SQL/NoSQL databases, and cloud platforms.
Ability to reason deeply about model behavior, experimental results, and data quality.
Excitement to work in person in San Francisco, five days a week (with optional remote Saturdays), and thrive in a high-intensity, high-ownership environment.

NICE TO HAVE

Real-world post-training team experience in industry (highest priority).
Publications at top-tier conferences (NeurIPS, ICML, ACL).
Experience training models or evaluating model performance.
Experience in synthetic data generation, LLM evaluations, or RL-style workflows.
Work samples, artifacts, or code repositories demonstrating relevant skills.

BENEFITS