Researcher, Alignment Training

OpenAI OpenAI · AI Frontier · San Francisco, CA · Research

Researcher focused on studying and shaping aligned behavior in frontier AI models through various training stages (pre-training, mid-training, post-training). The role involves developing synthetic data methods, building evaluation loops, designing data generation pipelines, and creating experiments to distinguish durable learned behavior from artifacts. Collaboration with other teams is key to translate research insights into better model behavior.

What you'd actually do

  1. 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.
  2. Study how pre-training, mid-training, and post-training each shape downstream model behavior, and which interventions are best applied at which stage.
  3. Build evaluation loops that connect model behavior back to training data and training objectives, so the team can iterate faster and with clearer signal.
  4. Design reusable data generation and filtering pipelines that improve the quality, diversity, and robustness of training data.
  5. Create experiments that distinguish durable learned behavior from benchmark gains, distribution-specific effects, or evaluation artifacts.

Skills

Required

  • large-scale model training
  • synthetic data
  • evaluation
  • pre-training
  • post-training
  • model behavior
  • training infrastructure
  • experimental design
  • data generation
  • data filtering
  • hypothesis formulation
  • pipeline building
  • experimentation
  • results analysis
  • communication

Nice to have

  • alignment
  • user intent understanding
  • instruction following
  • reasoning
  • honesty
  • reliability
  • robustness
  • durability

What the JD emphasized

  • exceptional technical depth
  • move from an ambiguous behavioral question to a concrete experimental program
  • formulate the hypothesis, design the intervention, build the pipeline, run the experiment, and decide whether the result is real
  • strong record of technically excellent work
  • comfortable designing experiments where the signal is subtle, noisy, or indirect
  • Can move between research taste and engineering execution
  • unusually good judgment about which research questions are worth pursuing and which signals are strong enough to trust
  • practical, evidence-driven work grounded in experiments

Other signals

  • studies how frontier models acquire durable behavioral tendencies
  • identifying which behaviors can be shaped through pre-training, mid-training, and post-training
  • building the data, objectives, and evaluations needed to influence them
  • determining whether the resulting behavior reflects a general learned tendency or a narrow artifact of the training distribution