Agent Post-Training, API & Power Users

Responsibilities

• Design and run experiments that improve model behavior in API and power-user workflows: function calling, tool use, coding, planning, long-horizon execution, factuality, instruction following, error recovery, and calibrated reasoning.
• Build evals, graders, and environments from real developer and power-user workflows, then turn observed failures into training data, model-behavior hypotheses, and shipped improvements.
• Partner with API and power-users to identify high-leverage behavior gaps and convert product signals into post-training interventions.
• Improve how models behave when composed into systems: using tools reliably, respecting developer intent, handling partial failures, asking for clarification when appropriate, and maintaining coherence across multi-step tasks.
• Own end-to-end model behavior projects, from qualitative failure analysis through data generation, training experiments, eval design, integration into major runs, and launch readiness.
• Develop feedback loops that use power-user traces, API usage patterns, and production-like environments to discover the next frontier of agentic model failures and gaps.
• Help decide which agentic capabilities, behavioral fixes, and partner-team integrations are ready for inclusion in major model runs.
• Debug hard failures in shipped or near-shipped models by moving between traces, evals, training data, model outputs, and product context.
• Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.
• Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.
• Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.

Requirements

• Strong technical fundamentals in ML, software engineering, systems, statistics, or applied research, and can quickly learn across unfamiliar parts of the stack.
• Hands-on experience with LLMs, post-training, RL/RLHF/RLAIF, evals, graders, synthetic data, coding agents, tool-using agents, API products, or production ML systems.
• Strong taste for model behavior: can look at a transcript, trace, eval failure, or API interaction and form concrete hypotheses about what the model needs to learn.
• Excited by ambiguous capability problems where the signal is noisy, the failures are qualitative, and the solution may involve data, training, evals, product changes, or all of the above.
• Deeply care about developer and expert-user experience, especially how models behave when embedded in real user workflows, API products, and agent harnesses.
• Comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.
• Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.
• Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.