OpenAI· Alignment· San Francisco
Researcher, Alignment Training
Comp$250K – $445K
Classified Tasks (22)
Automate 0%Augment 73%Human-Only 27%
Augment (16)
AI assists, human decides
Design data and training interventions to teach specified behavioral tendencies.
technical
Develop synthetic data methods that induce higher-level behavioral tendencies (e.g., intent understanding, instruction following, clear reasoning, honesty, goal-consistent actions).
technical
Build reusable data generation pipelines that improve the quality, diversity, and robustness of training data.
operational
Design filtering pipelines to remove low-quality or unhelpful training examples.
operational
Study how pre-training, mid-training, and post-training each shape downstream model behavior.
analytical
Build evaluation loops that connect observed model behavior back to training data and training objectives.
operational
Create experiments that distinguish durable learned behavior from benchmark gains, distribution-specific effects, or evaluation artifacts.
analytical
Design interventions and experimental protocols to test behavioral hypotheses.
technical
Build experiment pipelines and infrastructure to run large-scale training and evaluation studies.
operational
Run experiments at scale and collect experimental data and metrics.
operational
Analyze experimental results to decide whether observed behaviors are real, robust, and general.
analytical
Determine whether resulting behaviors reflect general learned tendencies or narrow artifacts of the training distribution.
analytical
Translate research insights into concrete changes to data, objectives, or training procedures to improve deployed model behavior.
operational
Measure whether models have learned underlying principles rather than surface patterns through targeted evaluations.
analytical
Study how models learn to interpret user intent, follow instructions reliably, reason through tasks, express uncertainty, act honestly, and remain reliable under new conditions.
analytical
Design evaluation methods and metrics that provide clearer signal for iterative research and training decisions.
analytical
Human-Only (6)
Requires human judgment
Shape the research agenda for alignment training by prioritizing which model behaviors to target.
leadership
Define target behaviors that models should learn and specify which should remain invariant or adapt across settings.
analytical
Determine which interventions are best applied at pre-training, mid-training, or post-training stages.
analytical
Formulate hypotheses from ambiguous behavioral questions to produce concrete experimental programs.
analytical
Collaborate with pre-training, post-training, alignment, and product-facing teams to implement research findings.
communication
Ensure desirable behavioral tendencies emerge early in training, strengthen throughout training, and appear robustly in deployed systems.
operational
Job description
Researcher, Alignment Training | OpenAI Careers ## Researcher, Alignment Training Alignment - San Francisco Apply now(opens in a new window) **About The Team** The Alignment Training team studies how frontier models acquire durable behavioral tendencies across the training stack. We work on identifying which behaviors can be shaped through pre-training, mid-training, and post-training; building the data, objectives, and evaluations needed to influence them; and determining whether the resulting behavior reflects a general learned tendency or a narrow artifact of the training distribution. Our work spans synthetic data, pre-training, mid-training, post-training, model behavior, and evaluation. We study how models learn to interpret intent, follow instructions, reason through tasks, express uncertainty, act honestly, and remain reliable under new conditions. The goal is to make desirable tendencies emerge early, strengthen throughout training, and appear robustly in deployed systems. **About The Role** We’re looking for a senior researcher with exceptional technical depth in large-scale model training, synthetic data, or evaluation who is excited to study how training choices shape aligned behavior in frontier models. You will help shape the research agenda for alignment training: defining the behaviors we want models to learn, designing data and training interventions to teach them, and building the evaluation loops needed to tell whether those behaviors are broad, robust, and durable. The strongest candidates will be able to 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. This role is especially well suited for someone who wants to work close to the core model training loop, where choices about data, objectives, and evaluation directly shape how aligned deployed systems are. **In this role, you'll:** * 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. **You might thrive in this role if you:** * Have a strong record of technically excellent work in large-scale ML, especially in pre-training, post-training, synthetic data, model evaluation, or training infrastructure. * Are 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. * Have 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. * Are excited by alignment proble