Machine Learning Research Scientist, Reasoning

Scale AI Scale AI · Data AI · San Francisco, CA · Research

Machine Learning Research Scientist focused on reasoning in LLMs, specifically for agentic systems like browser and software engineering agents. The role involves studying critical data types, identifying effective data sources and methodologies to improve LLM reasoning, and contributing to research while collaborating with engineering teams to implement solutions.

What you'd actually do

  1. You will contribute to impactful research on language model reasoning
  2. collaborate with external researchers
  3. work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions
  4. study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents
  5. identify the most effective data sources and methodologies for improving LLM reasoning

Skills

Required

  • PyTorch, JAX, or TensorFlow
  • interpreting research literature
  • turning new ideas into working prototypes
  • LLM capabilities and reasoning
  • written and verbal communication skills
  • work effectively across teams

Nice to have

  • fine-tuning open-source LLMs
  • leading bespoke LLM fine-tuning projects using PyTorch/JAX
  • building applications and evaluations related to LLM-based agents
  • tool-use
  • text-to-SQL
  • browser agents
  • coding agents
  • GUI agents
  • agent frameworks such as OpenHands, Swarm, LangGraph, or similar
  • advanced agentic reasoning techniques such as STaR and PLANSEARCH
  • cloud-based ML development
  • AWS or GCP environments

What the JD emphasized

  • track record of published research in top ML and NLP venues
  • at least three years of experience solving complex ML challenges
  • research and practical experience in building applications and evaluations related to LLM-based agents

Other signals

  • researching LLM reasoning
  • improving LLM reasoning
  • advancing LLM-based agents
  • data strategy for LLM reasoning