Research Engineer, Evals & RL Environments

Nolla Health

New York (NY)

On-site

USD 180,000 - 240,000

Full time

14 days+

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Benefits offered by this job

Health Insurance

HSA/FSA Eligible

Flexible, unlimited vacation

Meal stipends

Team retreats

Job summary

Nolla Health is building AI-powered healthcare evaluation environments and data pipelines to turn clinical knowledge into trustworthy benchmarks and training assets. We’re seeking a research engineer to design reproducible tasks, patient-state environments, and robust data processing.

You will collaborate with clinicians and engineers to define tasks, validate results, and ensure privacy and data governance while contributing to scalable evaluation tooling and product improvements in NYC.

Qualifications

Strong programming skills and experience building reliable software for experiments, data processing, or agent systems.
Hands-on experience building an agent evaluation harness, an RL environment, or closely related infrastructure.
The ability to translate an open-ended problem into a runnable task with measurable outcomes.
Careful judgment about data quality, reproducibility, grading reliability, and what a benchmark result actually supports.
Comfort debugging across datasets, environment state, agent behavior, and scoring code.
The ability to work with clinicians and turn domain expertise into precise specifications and tests.
Evidence of independent execution through shipped systems, research implementations, open‑source work, or projects you’ve owned.

Responsibilities

Build harnesses that run agents through reproducible tasks, capture their actions, and make failures easy to investigate.
Develop multi-step and multi-visit environments with patient state, clinical tools, and realistic consequences for an agent’s decisions.
Work with clinicians to turn clinical workflows into well-defined tasks, rubrics, and success criteria.
Build data pipelines for preparing, validating, versioning, and tracing evaluation and training datasets.
Develop scoring and reward mechanisms, combining programmatic checks, model-based grading, and clinician review where appropriate.
Test graders and environments for ambiguity, shortcuts, data leakage, and reward hacking.
Run experiments, analyze trajectories, and identify whether changes improve performance across meaningful slices of cases.
Work with our applied AI engineer to turn evaluation findings into product improvements.
Build with privacy, access controls, and permitted data use in mind.

Skills

Programming

Agent evaluation

Task specification

Data quality

Debugging

Clinician collaboration

Independent execution

Tools

RL Environments

Simulation tools

Data pipelines

Job description

New York City · Full-time

Build the environments that teach us what healthcare AI can do—and where it needs to improve.

At Nolla Health, we’re building AI-powered healthcare. To improve the systems involved in delivering care, we need realistic ways to test them: cases that unfold over time, tools an agent can use, and clear criteria for judging what happens.

We’re looking for a research engineer to build evaluation harnesses, simulated environments, and data pipelines that turn clinical knowledge and appropriately governed data into useful evaluation and training assets.

You’ll work with engineers and clinicians to define tasks, build the systems that run them, and make the results trustworthy. The work will support our own agents and models, with the opportunity to develop benchmarks and environments for external AI teams.

What you'll do

Build harnesses that run agents through reproducible tasks, capture their actions, and make failures easy to investigate.
Develop multi-step and multi-visit environments with patient state, clinical tools, and realistic consequences for an agent’s decisions.
Work with clinicians to turn clinical workflows into well-defined tasks, rubrics, and success criteria.
Build data pipelines for preparing, validating, versioning, and tracing evaluation and training datasets.
Develop scoring and reward mechanisms, combining programmatic checks, model-based grading, and clinician review where appropriate.
Test graders and environments for ambiguity, shortcuts, data leakage, and reward hacking.
Run experiments, analyze trajectories, and identify whether changes improve performance across meaningful slices of cases.
Work with our applied AI engineer to turn evaluation findings into product improvements.
Build with privacy, access controls, and permitted data use in mind.

What you bring

Strong programming skills and experience building reliable software for experiments, data processing, or agent systems.
Hands‑on experience building an agent evaluation harness, an RL environment, or closely related infrastructure.
The ability to translate an open-ended problem into a runnable task with measurable outcomes.
Careful judgment about data quality, reproducibility, grading reliability, and what a benchmark result actually supports.
Comfort debugging across datasets, environment state, agent behavior, and scoring code.
The ability to work with clinicians and turn domain expertise into precise specifications and tests.
Evidence of independent execution through shipped systems, research implementations, open‑source work, or projects you’ve owned.

We expect many candidates to have roughly two to six years of relevant experience, but we’re flexible for someone with exceptional work. Research experience is valuable; you should also enjoy building and maintaining the systems behind the experiments.

Bonus points

RL environments, simulated tool use, or long‑horizon agent benchmarks.
Synthetic data generation, human review workflows, or dataset quality systems.
Reward design, model‑based graders, and validation against expert judgments.
Building evaluation or training data products for AI labs.
Healthcare data, clinical reasoning evaluations, or longitudinal patient workflows.

Healthcare experience is welcome, but it isn’t required. Curiosity about the domain and a willingness to work closely with clinicians matter.

You’ll help define how we measure progress toward AI that can support care over time. Your work will make it possible to investigate failures, compare approaches, and build training signals grounded in realistic tasks.

You’ll own both the experimental design and the engineering needed to make it useful, with room to shape a new area of work at Nolla.

We’d ideally like you to be based in New York City.

Role logistics, compensation & benefits