Research Engineer
$210k - $450k • Remote • San Francisco • FullTime
Posted 1mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Research Engineer
About the job
AfterQuery is an applied research lab focused on curating data solutions for foundation model development, aiming to make expertise widely accessible. We work directly with customers building foundation models, playing a crucial role in their improvement. This is a unique opportunity to join a rapidly growing AI company, reportedly a fast-approaching unicorn with significant backing from top investors and individuals from leading AI labs. As a Research Engineer, you will design and execute training experiments to understand how datasets impact model behavior, using controlled SFT and RL experiments to measure the effects of data on capability, generalization, and alignment. You will manage the entire experiment lifecycle, from hypothesis formulation and pipeline building to analysis and iteration, translating data into evidence of model improvement.
Responsibilities
- Design and build post-training infrastructure for SFT, RL, and evaluation workflows.
- Build reliable pipelines for data preparation, dataset versioning, sampling, training, checkpoint management, and evaluation.
- Develop experiment orchestration and tracking systems for reproducible, comparable, and debuggable runs.
- Create reusable abstractions for researchers to quickly launch experiments across various datasets, models, and training recipes.
- Integrate systems with partner labs' training stacks, model APIs, compute environments, and evaluation infrastructure.
Requirements
- At least 2 years of professional experience in machine learning engineering, research engineering, ML infrastructure, or a 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 LLM fine-tuning, post-training, and evaluation.
- Ability to build reliable, reproducible systems for launching and comparing ML experiments.
- Strong debugging skills across distributed systems, data pipelines, training infrastructure, and model behavior.
- Understanding of experimental design and ability to extract actionable conclusions from noisy results.
- Ability to move quickly between infrastructure engineering and hands-on experimentation.
- A bias toward building, testing, and shipping.
Benefits
- Health Insurance: Medical, Vision, Dental
- 401(k) with Employer Match
- Daily Meals: Daily UberEats Stipend
- Monthly Wellness Stipend
- Meaningful equity