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SimpleClosure

AI/ML Engineer, RL Environments - Asset Hub

Hybrid in New York City (Remote)$140k–$200kfulltimemidAdded today

About this role

SimpleClosure is hiring an AI/ML Engineer to transform real-world assets acquired from shuttered companies into high-value AI-training products—RL environments, agentic task suites, evals, and datasets. You'll prototype and productionize pipelines that convert production codebases and databases into derivative works for AI labs and agent builders who are already customers.

What you'll do

  • Identify opportunities to convert raw assets (codebases, workspaces, databases) into AI-training products such as RL environments and evaluation benchmarks
  • Design and build pipelines for asset transformation including repository ingestion, test harnesses, Docker reproducibility, and verifier scripts
  • Create interactive sandbox environments using MCP servers and browser automation layers for agent training and evaluation
  • Assess commercial viability of assets by mapping them to current buyer demand and maximizing derivative product value
  • Prototype quickly, then scale successful approaches into repeatable, production-ready workflows
  • Collaborate with buyers, labs, and technical stakeholders to align product development with training needs while managing security, privacy, and licensing concerns

What they're looking for

  • RL environments and/or AI-training product development (critical)
  • Python and container technologies (Docker)
  • CI/test infrastructure and reproducible sandboxing
  • LLM evaluation frameworks and agent harnesses (e.g., SWE-bench-style, Verifiers)
  • Verifier and reward function design
  • Repository and codebase analysis
  • Data privacy, security, and PII handling
  • Full-stack product ownership from concept to production
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SimpleClosure

SimpleClosure builds a platform that simplifies the business shutdown process. The company is hiring full stack engineers to develop frontend and backend features that enhance their platform.

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Likely interview questions

  • Walk us through a RL environment or evaluation product you built from scratch—how did you design the task distribution and reward function?
  • Describe your experience converting unstructured codebases or datasets into usable training artifacts. What were the biggest challenges?
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