AI/ML Engineer, RL Environments - Asset Hub

SimpleClosure • New York, NY

10d • $140,000 - $200,000 • Hybrid

About The Position

SimpleClosure is seeking an AI/ML Engineer to turn Asset Hub’s one-of-a-kind inventory, and existing AI buyer relationships, into high-value AI-training products. When companies shut down, we acquire the real assets they built — production codebases, workspaces, and databases. Your job is to find the opportunities hidden in that inventory and build them: transforming real-world assets into derivative works — reinforcement-learning environments, agentic task suites, evaluations and verifiers, and training datasets — that are far more valuable to the AI labs, RL-environment providers, and agent builders who already buy from us.
This is a hands-on building role for someone who comes from the RL-environments and AI-training world and has actually created environments and products used to train or evaluate models — that background is essential. You’ll prototype fast, then harden what works into repeatable pipelines, working in a small, dedicated Asset Hub pod alongside product, engineering, and the GM of Asset Hub.
Candidates MUST be located in the New York City Metro area.

Requirements

• 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.
• Education: Bachelor’s or Master’s in Computer Science, Machine Learning, or a related field — or equivalent practical experience.

Nice To Haves

• Contributions to public benchmarks or eval frameworks
• Experience with post-training / fine-tuning data
• Simulation or frontend skills (MCP, Playwright) for world-building
• Team Management: experience building and managing a team of engineers, a plus.

Responsibilities

• 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.

Benefits

• Base Salary: $140,000 - $200,000, depending on experience level
• 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 in office)
• Two company-wide offsites each year
• 401(k) with Traditional and Roth options, with immediate eligibility

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree