AI Engineer

Requirements:

• Our current Core AI environment includes Python cloud services; React and TypeScript product surfaces; asynchronous and streaming systems; typed APIs and data models; relational and vector data; durable workflows; protocol-based tool execution; multiple model providers; and cloud-native deployment.
• We use coding agents, automated tests, traces, evaluations, browser automation, offline replay, cost and latency signals, and production feedback as part of everyday engineering. We are building toward a governed collect, learn, evaluate, and apply loop rather than a single monolithic training system.
• This is context, not a checklist. We do not require previous experience with every language, framework, model provider, cloud platform, database, or infrastructure tool in our stack. Comparable experience building distributed runtimes, experimentation platforms, retrieval or recommendation systems, workflow engines, developer platforms, or production AI systems is highly relevant.
• 5-12 years of software engineering experience. We will make exceptions for exceptional people in either direction.
• Strong backend and distributed-systems fundamentals, including typed API design, asynchronous workflows, persistence, reliability, and production debugging.
• Practical experience shipping LLM, agent, search, retrieval, recommendation, experimentation, workflow, or developer-platform systems into production.
• An empirical approach to AI quality: you can form a hypothesis, design a useful evaluation, interpret noisy evidence, and distinguish a real improvement from movement in a proxy metric.
• The ability to reason about retries, idempotency, partial failure, long-running state, concurrency, latency, cost, experiment design, and safe rollout.
• A security-minded approach to multi-tenant systems, identity, authorization, credentials, tool execution, privacy, and auditability.
• Product judgment. You can find a durable abstraction behind a real requirement without generalizing too early or freezing customer-specific behavior into the platform.
• AI-native. You use coding agents and modern models as a force multiplier while still owning architecture, correctness, evidence, and operational outcomes.
• Clear communication and high EQ. You can work with product, infrastructure, customer engineering teams, and applied AI researchers without losing technical depth.
• A generalist mindset and comfort moving between libraries, services, data, experiments, integrations, evaluations, and product surfaces.
• Comfort building product-facing software. You can work in React and TypeScript when a capability needs a great interface, and you can reason about streaming state, accessibility, and end-to-end user experience.
• Comfort with startup ambiguity, fast feedback loops, and broad ownership.
• Experience with reinforcement learning, preference learning, reward modeling, post-training, continual learning, ranking, causal inference, or large-scale experimentation is valuable but not required. We care more about whether you can connect learning ideas to production evidence and reliable systems than whether you have used a particular technique.
• Engineers from agent-runtime, distributed-workflow, retrieval, recommendation, experimentation-platform, integration-platform, developer-platform, or production applied-AI backgrounds often do well here. We care more about the systems you personally built, measured, and operated than a particular company, school, language, or model vendor.