AI Engineer

HockeyStack is building the agent infrastructure for enterprise revenue. We spent five years building the only data architecture that preserves causality across the full revenue stack — every interaction, every signal, in sequence. On top of that foundation, we built Nex-lm, a purpose-built AI engine that compiles natural language into deterministic agent workflows. The result is a platform that can extract the revenue blueprint from a company's data, encode it into repeatable automations, and execute it across sales, marketing, and customer success — consistently, at scale. We are not building a dashboard tool with an AI feature. We are building the operating layer that replaces the human bottleneck in enterprise revenue organizations. This is a category being defined right now, and we intend to own it. We have raised $50M+ from Bessemer Venture Partners, General Catalyst, Y Combinator, and others. We operate fully in-person in San Francisco . We move fast and we hire people who want to win. Since launching late 2023, we have grown to 8-figures in ARR , process over 60 TB of revenue data monthly , and we are working with some of the largest B2B companies in the world like Microsoft, Harvey, New Relic, Collibra , etc. 🚀 Your Mission As an AI Engineer your mission is to harness the power of agentic AI in building the single portal where GTM team members go to grow their business. You’ll push the boundaries of what LLMs can do — designing systems that don’t just respond to prompts but autonomously orchestrate complex sales and marketing workflows, learn from outcomes, and get sharper over time. 🔧 What You’ll Do Own and continuously improve the backend systems that power all agentic AI features across the platform Design, write, and maintain agents responsible for different tasks across a GTM team’s workflows, leveraging the latest agent frameworks Develop and maintain backend services in Node.js that power these agents Build state-of-the-art context management pipelines and manage thousands of stateful agents. Ship fast and often — deploying to production frequently while maintaining high-quality standards 🧬 What We’re Looking For Ownership-first mindset — you take initiative, move fast, and figure things out Thrive in early-stage, high-urgency environments where speed and impact matter Fully committed to working in-person 5 days/week at our SF HQ Curious, self-aware, and feedback-driven — you bring energy, not ego See this role as a defining chapter — not a stepping stone or side quest What you bring: Hands-on experience building and deploying LLM-powered applications or agentic AI systems in production Strong backend engineering experience with Node.js and modern JavaScript/TypeScript Experience designing and orchestrating multi-agent systems , including managing agent state, memory, and context across complex workflows Familiarity with modern AI frameworks and providers ( Braintrust, Cerebras , Langchain or similar) Understanding of multi-model architectures — knowing when and how to route tasks across different LLMs for cost, speed, and quality tradeoffs A track record of shipping in fast-moving, production environments ✨ Why Join Now We’re at an inflection point. The product is proven, the market is massive, and the opportunity is wide open. You’ll be joining a company with real traction, rapid growth, and meaningful backing — where every person still shapes the outcome. This isn’t just a job. It’s a chance to build something category-defining with people who care deeply about doing it right. As part of our San Francisco, California-based team, the salary range for this role is $180,000- $220,000 USD base pay annually , depending on experience and qualifications. HockeyStack is proud to be an Equal Opportunity Employer . We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other legally protected status. We celebrate diversity and are committed to fostering an inclusive environment for all employees.

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