Job Description

Help Shape the Future of AI Reasoning and Agentic Intelligence

Scale AI is hiring a Machine Learning Research Scientist, Reasoning to advance the next generation of large language model (LLM) reasoning systems, AI agents, and scalable evaluation frameworks.

This role sits at the cutting edge of AI research and practical implementation, focusing on improving reasoning capabilities within LLMs, browser agents, coding agents, software engineering agents, and autonomous AI systems.

You will collaborate with world-class researchers and engineers to develop advanced data strategies, reasoning methodologies, and agentic workflows that accelerate progress toward Artificial General Intelligence (AGI).

This is an ideal opportunity for machine learning researchers, NLP scientists, reasoning specialists, and LLM engineers passionate about frontier AI research, agentic reasoning, and large-scale model development.

About Scale AI

Scale AI is one of the world’s leading AI infrastructure and data platforms, powering advancements in:

• Generative AI
• Autonomous vehicles
• Defense technologies
• Enterprise AI systems
• Government AI applications
• Large language model evaluation

For more than eight years, Scale AI has helped build reliable AI systems through high-quality training data, model evaluation, and scalable AI infrastructure.

Following its recent Series F funding round, Scale AI continues expanding its capabilities to advance AGI development and establish new standards in AI model evaluation and reasoning.

Scale partners with organizations including:

• Meta
• Cisco
• DLA Piper
• Mayo Clinic
• U.S. government agencies including the Army and Air Force

Role Overview

As a Machine Learning Research Scientist focused on reasoning, you will study and develop the data, architectures, and methodologies necessary to improve advanced LLM reasoning and agent behavior.

You will help define:

• High-quality reasoning datasets
• Agent evaluation methodologies
• Novel planning and reasoning strategies
• Scalable data generation systems
• Real-world AI deployment workflows

The role combines:

• Frontier AI research
• Experimental prototyping
• Model evaluation
• Cross-functional collaboration
• Production-oriented machine learning development

You will work closely with engineering and research teams to transform cutting-edge research into scalable AI systems used in real-world applications.

Key Responsibilities

AI Reasoning Research

• Conduct advanced research into reasoning capabilities within large language models (LLMs).
• Explore novel approaches to planning, tool usage, and agentic reasoning.
• Develop methodologies for improving model reasoning quality and reliability.

LLM Agent Development

• Build and evaluate:
  • Browser agents
  • Coding agents
  • GUI agents
  • Software engineering agents
  • Tool-using AI systems
• Design evaluation frameworks for autonomous agent behavior.

Data Strategy & Model Evaluation

• Identify optimal data sources for reasoning-focused training and evaluation.
• Develop scalable data generation and evaluation pipelines.
• Improve AI model benchmarking and reasoning assessment systems.

Research Prototyping

• Rapidly implement and test new machine learning ideas.
• Translate research papers into production-ready prototypes.
• Experiment with novel architectures and reasoning workflows.

Cross-Functional Collaboration

• Partner with research scientists, ML engineers, and product teams.
• Collaborate with external researchers and academic contributors.
• Communicate technical findings clearly across teams.

Required Qualifications: Essential Requirements

• Practical experience working with large language models (LLMs).
• Strong proficiency in:
  • PyTorch
  • JAX
  • TensorFlow
• At least 3 years of experience solving complex ML challenges in:
  • Research environments
  • Applied AI systems
  • Product development
• Published research in leading AI/ML conferences such as:
  • ACL
  • EMNLP
  • NAACL
  • NeurIPS
  • ICML
  • ICLR
  • CoLLM
• Strong understanding of:
  • LLM reasoning
  • Planning algorithms
  • Agentic AI systems
  • NLP research methodologies
• Excellent written and verbal communication skills.

Preferred Qualifications: Nice-to-Have Experience

• Fine-tuning open-source LLMs at scale.
• Experience building AI agents using frameworks such as:
  • LangGraph
  • OpenHands
  • Swarm
• Familiarity with advanced reasoning methods including:
  • STaR
  • PLANSEARCH
• Experience with:
  • Text-to-SQL systems
  • Browser automation agents
  • Coding assistants
  • Tool-use agents
• Cloud ML development experience using:
  • Amazon Web Services
  • Google Cloud

Research Interview Process

Scale AI’s research interviews evaluate:

• Machine learning prototyping skills
• Model debugging ability
• Research depth and reasoning expertise
• Cross-functional collaboration
• Technical communication

This role does not include LeetCode-style coding assessments.

Compensation & Benefits

Compensation

• Base salary range:
  • USD $252,000 – $315,000 annually
• Compensation may include:
  • Equity grants
  • Performance incentives
  • Comprehensive benefits

Salary depends on:

• Experience
• Skills
• Interview performance
• Education
• Work location

Benefits Include

• Comprehensive health coverage
• Dental and vision insurance
• Retirement benefits
• Learning & development stipend
• Generous paid time off
• Potential commuter stipend
• Equity-based compensation eligibility

Why Join Scale AI?

Work on Frontier AI Problems

Contribute directly to advanced reasoning systems and next-generation LLM agents.

Influence AGI Development

Help define the data and methodologies shaping future artificial intelligence systems.

Collaborate with Elite Researchers

Work alongside world-class researchers, engineers, and AI innovators.

High Research Impact

Publish impactful work while contributing to production-grade AI systems used globally.

Inclusive Workplace

Scale AI is committed to building an inclusive, diverse, and equal opportunity workplace where every employee can thrive.

Apply to Machine Learning Research Scientist, Reasoning.