Research Engineer
Research Engineer designing post-training infrastructure and running controlled experiments to measure how datasets affect foundation-model behavior. Requires at least 2 years of ML or research engineering experience, strong Python, and hands-on experience with PyTorch, JAX, Ray, Slurm, and LLM post-training.
About the job
Responsibilities
- Design and build post-training infrastructure for supervised fine-tuning (SFT), reinforcement learning (RL), and evaluation workflows.
- Build reliable pipelines for data preparation, dataset versioning, sampling, training, checkpoint management, and evaluation.
- Develop experiment orchestration and tracking systems that make runs reproducible, comparable, and easy to debug.
- Create reusable abstractions for launching experiments across datasets, models, and training recipes.
- Integrate systems with partner-lab training stacks, model APIs, compute environments, and evaluation infrastructure.
- Design and run controlled training experiments to measure how data sources, structures, and selection strategies affect model capability, generalization, and alignment.
- Formulate hypotheses, build pipelines, run models, analyze results, identify confounders, and determine next steps.
- Debug distributed systems, data pipelines, training infrastructure, and model behavior.
Requirements
- At least 2 years of professional experience in machine learning engineering, research engineering, ML infrastructure, or a closely related field; 2–4+ years preferred.
- Strong Python and software engineering skills.
- Hands-on experience with PyTorch, JAX, Ray, and Slurm.
- Experience building production-quality ML training, evaluation, or data infrastructure.
- Hands-on experience with large language model fine-tuning, post-training, and evaluation.
- Ability to build reliable, reproducible systems for launching and comparing ML experiments.
- Understanding of experimental design and ability to extract actionable conclusions from noisy results.
- Ability to move quickly between infrastructure engineering and hands-on experimentation.
- Bias toward building, testing, and shipping.
Benefits
- Medical, vision, and dental insurance.
- 401(k) with employer match.
- Daily meals and UberEats stipend.
- Wellness stipend, including monthly Equinox membership coverage.
- Commuting costs covered.
Skills
Python, PyTorch, JAX, Ray, Slurm, Machine Learning, ML Infrastructure, Llm Fine-Tuning, Reinforcement Learning, Supervised Fine-Tuning, Experiment Tracking, Distributed Systems, Data Pipelines, Model Evaluation, Experimental Design
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