Member of Technical Staff (New Grad)

New Yorkfull timeOn Site

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senior

$140k–$175k

H1B: no

Ambral The Member of Technical Staff at Ambral builds infrastructure to convert enterprise historical data into scalable, continuous model-improvement systems. This role involves working hands-on with customers and developing replayable environments and evaluation sets.

Responsibilities

We’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.

Qualification

You care about reproducibility

Required

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.