> Markdown version of [/jobs/ext/2719007-ai-ml-engineer](https://www.wearedevelopers.com/jobs/ext/2719007-ai-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** SIMPLE CLOSURE INC. - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $140,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, Large Language Models, Build Management, Information Technology, Playwright, Front End Software Development, Docker - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/ai-ml-engineer-rl-environments-asset-hub-simpleclosure-8786892 ## About the Role * 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. ## Description * 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. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Boost Productivity with AI: Figma & Playwright MCP Workflows - Aris Markogiannakis](https://www.wearedevelopers.com/videos/1768-boost-productivity-with-ai-figma-playwright-mcp-workflows-aris-markogiannakis) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [You are not an AI developer](https://www.wearedevelopers.com/videos/1148-you-are-not-an-ai-developer) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)