Research Engineer
San Francisco$210k - $450kFull-timePosted 1mo ago
About the role
Design and run training experiments to isolate the impact of datasets on model behavior, conducting controlled SFT and RL-based post-training experiments to measure how data sources, structures, and selection strategies affect capability, generalization, and alignment. Own the full experiment loop from hypothesis to results, working with partner labs and internal teams to turn datasets into defensible evidence.
Responsibilities
- Design and build our 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 that make runs reproducible, comparable, and easy to debug.
- Create reusable abstractions that allow researchers to launch experiments quickly across datasets, models, and training recipes.
- Integrate our systems with partner-lab training stacks, model APIs, compute environments, and evaluation infrastructure.
Qualifications
- 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 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 the 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.
Skills
Required:PythonSoftware engineeringPyTorchJAXRaySlurmLLM fine-tuning
Benefits
- Health Insurance: Medical, Vision, Dental
- 401(k) with Employer Match
- Daily Meals: Daily UberEats Stipend
- Monthly Wellness Stipend
Last updated Sep 30, 2026. Interested? Apply on the company site.
About AfterQuery
AfterQuery is an applied research lab curating data solutions to accelerate foundation model development.
AI researchdata solutionsfoundation model developmenttraining datasupervised fine-tuningreinforcement learningagent environmentscomputer use trajectories
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