AI/ML Engineer, RL Environments – Asset Hub

SimpleClosure

AI/ML Engineer at SimpleClosure transforming unique assets into high-value AI-training products. Collaborating with teams to build and refine innovative reinforcement-learning environments and training datasets.

Posted 7/22/2026full-timeNew York City • New York • 🇺🇸 United StatesMid-LevelSenior💰 $140,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building Reinforcement Learning (RL) environments and AI-training products, with strong capabilities in Python, Docker, and CI/test infrastructure. Proven ability to design scalable pipelines and effectively communicate technical concepts to stakeholders.

Highest-signal resume keywords

Reinforcement Learning EnvironmentsPython ProgrammingDocker ContainerizationModel EvaluationTeam Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningAI-Training DataModel Evaluation EcosystemPythonDockerCI InfrastructureVerifier DesignReward DesignBenchmarkingTask Extraction

Soft Skills

Clear CommunicationOwnershipProblem-Solving

Tools & Technologies

PlaywrightMCP ServersQA ToolingSandboxed Application State

Industry Keywords

AI-Training ProductsAgentic Task SuitesEval FrameworksProprietary DatasetsPrivacy Handling

Tech Stack

Tools & technologies

DockerPython

About the role

Key responsibilities & impact

Take Asset Hub’s unique real-world assets — production codebases, workspaces, and databases — and identify how each can become a high-value AI-training product: RL environments, agentic task suites, evals and verifiers, benchmarks, and fine-tuning or trajectory datasets.
Design and build the pipeline that turns a raw asset into a derivative work: repository ingestion, test harnessing, commit-mining for task extraction, Docker/sandbox reproducibility, verifier and reward scripts, and QA tooling.
Wrap real data in interactive environments — sandboxed application state, MCP servers, and browser/Playwright layers — that buyers can train and evaluate agents against.
Spot the commercial opportunity in the inventory: which assets map to current lab and RLE demand, and what derivative product maximizes their value.
Prototype quickly, then harden the best ideas into repeatable, scalable pipelines so derivative-work creation isn’t one-off.
Partner with the Asset Hub buyer/BD side and directly with technical stakeholders at labs and RLE buyers to shape what we build to their training needs.
Work with sensitive material — codebases, workspace exports, and proprietary datasets — with strong attention to security, privacy, licensing, and PII handling.
Write clean, well-tested code and use AI tooling to move faster; collaborate closely with product, engineering, and the GM of Asset Hub.

Requirements

What you’ll need

RL-environments / AI-training background (critical): you’ve built RL environments and/or products used to train or evaluate models — environments, agentic task suites, evals, benchmarks, or verifiers. This is the core requirement, not a nice-to-have.
Experience: 4–8 years of engineering experience, with meaningful time in the RL-environments, AI-training-data, or model-evaluation ecosystem (at a lab, an RLE/eval company, or a team that shipped training environments or products).
Core engineering: strong Python, containers (Docker), and CI/test infrastructure; comfort building reproducible sandboxes from messy real-world code and data.
Evals & verification: familiarity with LLM evaluation and agent harnesses (SWE-bench-style setups, Verifiers, HUD, or similar) and with verifier/reward design, including resistance to reward hacking.
Ownership: a builder’s temperament — takes projects from concept to production, works scrappily (sometimes alongside contractors), and thrives in ambiguity.
Communication: a clear communicator who can be a credible technical face to lab and RLE researchers.
Nice to have: contributions to public benchmarks or eval frameworks; experience with post-training / fine-tuning data; simulation or frontend skills (MCP, Playwright) for world-building.
Education: Bachelor’s or Master’s in Computer Science, Machine Learning, or a related field — or equivalent practical experience.
Team Management: experience building and managing a team of engineers, a plus.

Benefits

Comp & perks

Competitive equity package
Comprehensive health benefits, including medical, dental, and vision
Life insurance
Unlimited paid time off
Flexible hybrid work environment in New York City, Midtown (currently 2 days per week)
Two company-wide offsites each year
401(k) with Traditional and Roth options, with immediate eligibility