Member of Technical Staff (Evals & Post-Training)

What we doAmbral Labs helps enterprises own the intelligence behind their most important workflows.Every company has years of historical evidence showing how work gets done: the context people had, the decisions they made, the actions they took, and the outcomes that followed. Today, most of that history is inert. It isn’t structured in a way that companies can use to evaluate models and improve agent behavior.Ambral turns this history into replayable environments and eval sets grounded in real workflows and observed outcomes. We use those environments to improve model performance through reinforcement learning and other post-training techniques, alongside context engineering, harness design, and agent engineering.The result is better, more cost-efficient AI for each enterprise’s specific work, powered by open-weight models that the company owns and controls. This allows each company to retain ownership of its core intelligence instead of outsourcing it to a model provider.We graduated from YC S2025, raised millions in funding, and are already deployed within multi-billion dollar enterprises. Now we're growing the founding team What you’ll doAt the center of Ambral Labs is a replayable environment engine for the enterpirse.The system reconstructs a company’s world as it existed at a particular moment in the past then exposes that state through the same tools an agent would use in production. This lets us place new policies and agent configurations inside real historical environments, observe how they reason and act, and grade their performance against real outcomes.You'll work across research, infrastructure, and production systems including:Building an environment factory that converts recorded enterprise data and task definitions into runnable environmentsDesigning graders that turn ambiguous business objectives into verifiable rewardsDeveloping methods for mining useful tasks, trajectories, and evaluation cases from historical workflowsCreating eval sets that are representative, reproducible, and resistant to overfittingFinding the right combinations of models, tools, context, and policies to maximize performance while reducing inference costTraining and evaluating agents that operate over long horizons, incomplete information, and large tool spacesBuilding replay and observability systems that make agent behavior explainable and measurableScaling from individual environments to thousands of concurrent training and evaluation runsThese problems are wide open. You’ll have significant ownership over both the research direction and the production systems that make it real.You’ll work directly with the CTO, deploy into real enterprise workflows, and see your research tested against consequential problems and observable outcomes. Who you areYou have 1-7 years of experience building production software or machine-learning systems (we're hiring at multiple levels for this role).Bonus points for working on reinforcement-learning environments, LLM post-training, evaluation infrastructure, agent harnesses, or closely related systemsYou understand how environment design, reward design, context, tooling, and policy behavior interactYou’re comfortable turning fuzzy business objectives into tasks and signals that can be evaluated reliablyYou can diagnose whether a model’s limitations come from the model itself, its context, its tools, its harness, or its trainingYou can move between research questions and production implementation without treating them as separate jobsYou write strong software and can build systems that process large, messy datasets at scaleYou care about reproducibility, observability, and understanding why a model behaves the way it doesYou’re looking to do the best work of your life and build something you’ll be proud of for decadesWe care much more about what you’ve built and how you think than credentials or conventional career paths.BenefitsSignificant equity and ownershipEquinox membershipFree meals, coffee, and snacksHealth insuranceUnlimited PTO