• AI Implementation - Design, build, and maintain all of our AI systems across the company. Everything from internal marketing, sales, finance, and operations systems to local AI systems in our engineering workflows, and the Chamber itself. • AI Adoption - Educate, Train, Support, and Uplift everyone in the company with their AI adoption. Running office hours, making tutorials, providing resources, sourcing solutions, and more. • AI Infrastructure - Architect and operate a scalable set of infrastructure that allows non-technical, and technical teams to use AI in a secure and productive manner. • AI Systems & Agents - In conjunction with departments, design, build and maintain all AI systems and agents. This includes administering and managing our off the shelf AI systems like Claude, Viktor, Cursor, etc. It also includes our custom built Agents, Skills, Plugins, Tools, and more. • AI Security - You are responsible for creating, maintaining, and enforcing all data, privacy, and security policies across our AI systems and Agents. Organization, Department, and Role based permissions and settings across all our products. • Other Projects - As this is a technical role, you will also be asked to help build other internal systems like automations, micro apps, and more.

Title: AI/ML Engineer, RL Environments - Asset Hub Location: Hybrid in New York City Department: Engineering Employment Type: Full time Location Type: Hybrid Job Description: 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 Asset Hub. 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 Asset Hub 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 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. Key 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. 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 - 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 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.

• Build high-performance API services and implement complex RAG and Agentic AI architectures. • Design and implement end-to-end RAG pipelines, including retrievers, vector stores (e.g., Pinecone, Weaviate, or pgvector), and generators. • Optimize latency and relevance to ensure production-grade performance. • Develop autonomous or semi-autonomous agents using frameworks such as LangChain, CrewAI, or Semantic Kernel. • Manage orchestration, tool integration, and robust error handling for non-deterministic AI outputs. • Assess performance, grounding accuracy, and hallucination detection using evaluation frameworks (e.g., RAGAS, TruLens).

Role Description Tiger Analytics is looking for a highly skilled AI Engineer with 7+ years of experience in software engineering, with a heavy focus on Python, AWS infrastructure, and Generative AI. The ideal candidate will be responsible for building high-performance API services and implementing complex RAG and Agentic AI architectures. Qualifications - Minimum of 7+ years of professional experience in software development and AI engineering. - Hands-on experience integrating Generative AI/LLM APIs, AWS Bedrock, and other model providers. - Experience with DevOps, CI/CD pipelines, and ML pipelines within the AWS ecosystem. - Exposure to building Gen AI/Agentic AI applications, managing efficiency, latency, and backend infrastructure. - Strong Python programming skills with a deep understanding of OpenAI API standards, JSON RESTful design, and LLM orchestration. - Experience working with Bedrock Agent/Core services is a significant plus. Requirements - Ability to design and implement end-to-end RAG pipelines, including retrievers, vector stores (e.g., Pinecone, Weaviate, or pgvector), and generators. - Expertise in latency optimization and relevance tuning to ensure production-grade performance. - Strategic approach to document chunking and embedding, balancing granularity with semantic coherence. - Practical experience developing autonomous or semi-autonomous agents using frameworks such as LangChain, CrewAI, or Semantic Kernel. - Ability to manage orchestration, tool integration, and robust error handling for non-deterministic AI outputs. - Proficiency in managing memory and context (episodic vs. long-term) in multi-turn interactions and external API interfacing. - Familiarity with evaluation frameworks (e.g., RAGAS, TruLens) to assess performance, grounding accuracy, and hallucination detection. - Ability to iterate systems based on performance metrics and continuous improvement practices. Benefits This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility. Company Description Tiger Analytics is a fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.