Research And Development Engineer

Uber AI Solutions is an enterprise marketplace connecting experienced industry professionals with Generative AI researchers and product teams working on advanced AI systems. The Research Engineer Curator will design, implement, review, and document complex benchmark tasks that evaluate how AI models plan, reason, code, experiment, and debug. The role also involves analyzing model behavior, identifying failure modes, and collaborating with researchers to improve evaluation methodologies.

Responsibilities:

• Design and curate challenging, multi-step Research Engineering tasks for AI model evaluation
• Create realistic agentic workflows that require planning, reasoning, coding, experimentation, and debugging
• Review and validate benchmark tasks to ensure technical correctness, difficulty, and reproducibility
• Collaborate closely with researchers through daily feedback sessions to continuously improve benchmark quality
• Perform data analysis and evaluate model behavior across complex research tasks
• Identify edge cases, failure modes, and opportunities to improve evaluation methodologies
• Document findings, evaluation criteria, and benchmark specifications

Requirements:

• MSc or PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Mathematics, Statistics, Physics, Engineering, or another STEM discipline
• Equivalent practical research experience in computational research domains will also be considered
• 1+ years of experience in Research Engineering, Machine Learning, AI Research, Data Science, or a related technical research role
• Experience working on research projects involving experimentation, coding, or quantitative analysis
• Strong Python programming and scripting skills
• Experience using Git and modern development workflows
• Familiarity with IDEs and debugging tools
• Knowledge of software engineering best practices and clean code principles
• Strong understanding of machine learning fundamentals
• Familiarity with Large Language Models (LLMs), their capabilities, limitations, and evaluation
• Experience designing, running, and analyzing ML experiments
• Strong analytical and statistical reasoning skills
• Experience cleaning, analyzing, and interpreting complex datasets
• Proficiency with Jupyter Notebooks or Google Colab
• Strong understanding of scientific methods, hypothesis testing, and experimental design
• Experience conducting rigorous computational research
• Enjoys solving difficult technical problems
• Has exceptional attention to detail
• Thinks creatively about edge cases and failure modes
• Can work independently in ambiguous research environments
• Produces high-quality, well-documented technical work
• Is comfortable collaborating closely with researchers in a fast-paced setting
• Basic understanding of reinforcement learning concepts
• Research Engineering or Research Scientist collaborations
• AI Evaluation or Benchmark Development
• Test Engineering or Quality Assurance
• LLM Red Teaming or AI Safety
• Prompt Engineering or Agentic AI workflows
• Experience using AI coding assistants (e.g., Gemini, Cursor, JetBrains AI Assistant, GitHub Copilot)