AI/ML Engineer, RL Environments - AssetHub at Simpleclosure

Job description

COMPANY OVERVIEW Most companies don’t end in acquisition — they end in closure. Yet shutdown has never been given the same rigor, clarity, or professionalism as the moments that came before it. Nearly 4 million companies shut down every year, creating a massive, overlooked market. SimpleClosure is building the infrastructure to change that. Having raised over $20M from leading VCs and trusted by more than 7,000 companies to date, we streamline the entire business dissolution process — from legal and tax to operational and administrative — as one coordinated effort instead of a patchwork of disconnected steps. We go a step further, helping companies sell their remaining assets and return the most value to their stakeholders, through our AssetHub. As the leading platform for company shutdowns, we have something no one else does: a continuous, proprietary flow of companies winding down, and the assets they built along the way. Our buyers span the top AI labs, RL environment providers, vertical and enterprise agent builders, VCs and deal-flow partners, domain marketplaces, and much more. Dissolution is our wedge; the marketplace is a big part of where we’re headed. Joining our AssetHub team means helping to define a new category from the ground up, working on complex, high-stakes problems, and building the products and processes that guide founders through one of the most consequential moments in a company’s life. JOB OVERVIEW SimpleClosure is seeking an AI/ML Engineer to turn AssetHub’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 AssetHub pod alongside product, engineering, and the GM of AssetHub. *Candidates MUST be located in the New York City Metro area. KEY RESPONSIBILITIES - Take AssetHub’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 AssetHub 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 AssetHub. DESIRED SKILLS & QUALIFICATIONS - 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. WHAT WE OFFER - Competitive compensation package consisting of a base salary and performance-based variable component. - 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 SIMPLECLOSURE IS AN EQUAL OPPORTUNITY EMPLOYER We are committed to providing a work environment free of discrimination and harassment. All employment decisions at SimpleClosure — including recruiting, hiring, promotion, compensation, and termination — are based on qualifications, merit, and business need, without regard to race, color, religion, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender identity or expression, national origin, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable federal, state, or local law. We’re committed to building a diverse and inclusive team, and we welcome applicants from all backgrounds. If you need a reasonable accommodation during the application or interview process, please let us know. APPLICATION PROCESS Thanks for applying! If your experience is a strong match for what we’re looking for, our hiring team will reach out to schedule an initial screening call and walk you through the rest of our interview process. We do our best to respond to every applicant, but given the volume of applications we receive, we may not be able to provide individual updates if we decide not to move forward.

Requirements

Must-haves

Candidates MUST be located in the New York City Metro area.
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 AssetHub 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 AssetHub.
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

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.
Team Management: experience building and managing a team of engineers, a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

goaimldockerpythonllmmachine learningrest

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off