What you'll doWe're building a replayable environment engine over real enterprise history.
The system reconstructs a company's context as it existed at any past time, 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 help build the infrastructure and work hands-on with customers to turn their real enterprise data into a scalable, continuous model-improvement system. The core problems include:
- Forward deploying with our customers to understand their tasks and data
- Developing the environment factory that converts recorded enterprise data and task definitions into runnable environments
- Designing graders to turn ambiguous business objectives into verifiable rewards
- Developing methods for mining useful tasks, trajectories, and evaluation cases from historical workflows
- Experimenting with learning objectives and task design
- Creating eval sets that are representative, reproducible, and resistant to overfitting
- Finding the right combinations of models, tools, context, and policies to maximize performance while reducing inference cost
- Building replay and observability systems that make agent behavior explainable and measurable
These problems are wide open. You'll have significant ownership over the production systems that make it real.
You'll work directly with the CTO, deploy into real enterprise workflows, and see your implementations tested against consequential problems and observable outcomes.
Who you are- You have 2+ years of experience building production software (ideally with experience in machine-learning systems)
- You write strong software and can build systems that process large, messy datasets at scale
- You're comfortable turning fuzzy business objectives into tasks and signals that can be evaluated reliably
- You can diagnose whether a model's limitations come from the model itself, its context, its tools, its harness, or its training
- You can move between research questions and production implementation without treating them as separate jobs
- You care about reproducibility, observability, and understanding why a model behaves the way it does
- You're looking to do the best work of your life and build something you'll be proud of for decades
We care much more about what you've built and how you think than credentials or conventional career paths.
Benefits- Significant equity and ownership
- Equinox membership
- Free meals, coffee, and snacks
- Health insurance
- Unlimited PTO
