Research Scientist, Machine Learning

Responsibilities

• check_circle Design and execute machine learning research experiments, including model architecture design, training recipe development, and rigorous evaluation across benchmarks and production metrics
• check_circle Develop and optimize ML models and algorithms targeting core research problems such as representation learning, generative modeling, or large-scale optimization
• check_circle Implement clean, reusable, and well-tested research code using modern ML frameworks such as PyTorch, contributing to shared research infrastructure
• check_circle Analyze experimental results to form data-driven conclusions, identify failure modes, and iterate on hypotheses in collaboration with research and engineering partners
• check_circle Contribute to the preparation and submission of research findings to peer-reviewed venues such as NeurIPS, ICML, ICLR, or CVPR
• check_circle Partner with cross-functional teams including product engineering, data science, and infrastructure to translate research advances into production-ready solutions
• check_circle Participate in code review processes to maintain research code quality, catching subtle issues including those arising from AI-assisted code generation
• check_circle Instrument experiments with appropriate logging, monitoring, and evaluation pipelines to ensure reproducibility and reliability of research outcomes
• check_circle Contribute to the research community through open-source releases, technical documentation, and knowledge sharing within the team

Basic qualifications

• Currently has, or is in the process of obtaining, a PhD in Computer Science, Statistics, Mathematics, Electrical Engineering, or a related quantitative field
• 2+ years of experience designing, training, and evaluating machine learning models using deep learning frameworks such as PyTorch or JAX
• Experience with Reinforcement Learning and/or Large Language Models (LLMs), including areas such as RLHF, policy optimization, or LLM fine-tuning
• Experience implementing and iterating on ML experiments end-to-end, from data pipeline construction through model evaluation and analysis
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred qualifications

• Demonstrated ability to contribute to open-source ML research codebases or reproducible research artifacts
• Experience applying ML research to large-scale production systems, including familiarity with A/B experimentation frameworks and staged rollout practices
• Experience training or fine-tuning large-scale foundation models, including familiarity with distributed training techniques such as model parallelism or mixed-precision training
• First-author or significant contributions to publications at peer-reviewed ML conferences such as NeurIPS, ICML, ICLR, CVPR, or EMNLP
• Interest or expertise in formal mathematics and theorem proving tools such as Lean

About the company