# Researcher, Alignment Training

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** AI Research
**Salary:** $250k – $445k/yr
**Skills:** Machine Learning, LLMs, Synthetic Data, Pre-Training, Post-Training, Model Evaluation, Training Infrastructure, Data Pipelines, Experiment Design, Alignment Research
**Posted:** 2026-04-30

> Senior researcher studies how training choices shape aligned behavior in frontier models, developing synthetic data, evaluation loops, and experiments to ensure durable, robust tendencies like honest reasoning and instruction-following.

## Job Description

## Responsibilities
- Develop synthetic data methods that teach models higher-level behavioral tendencies, such as understanding user intent, following instructions reliably, reasoning clearly, being honest, and acting consistently with intended goals and constraints.
- Study how pre-training, mid-training, and post-training each shape downstream model behavior, and which interventions are best applied at which stage.
- Build evaluation loops that connect model behavior back to training data and training objectives, so the team can iterate faster and with clearer signal.
- Design reusable data generation and filtering pipelines that improve the quality, diversity, and robustness of training data.
- Create experiments that distinguish durable learned behavior from benchmark gains, distribution-specific effects, or evaluation artifacts.
- Collaborate across pre-training, post-training, alignment, and product-facing teams to translate research insights into better model behavior.
- Help define the research agenda for alignment training: which behaviors should remain invariant across settings, which should adapt, and how to measure whether models have learned an underlying principle rather than a surface pattern.

## Requirements
- Strong record of technically excellent work in large-scale ML, especially in pre-training, post-training, synthetic data, model evaluation, or training infrastructure.
- Comfortable designing experiments where the signal is subtle, noisy, or indirect.
- Can move between research taste and engineering execution: forming hypotheses, building pipelines, running experiments, analyzing results, and turning findings into the next iteration.
- Unusually good judgment about which research questions are worth pursuing and which signals are strong enough to trust.
- Care about making models more useful, honest, steerable, and reliable for real users.
- Excited by alignment problems, even if you have not worked in alignment before.
- Communicate clearly across research, engineering, and product contexts.
- Prefer practical, evidence-driven work grounded in experiments.

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