Integration Engineer

About Hark Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory. We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world. To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems. About the Role You'll build and own the integrations layer that makes Hark's AI agent genuinely useful in the real world. That means connecting it to the tools, services, and data sources people actually use every day. That means designing and shipping the protocols, connectors, and execution primitives that let Hark take real actions: reading and writing email, managing calendars, interacting with third-party APIs, and coordinating across services on a user's behalf. The power of an agentic system is only as broad as what it can reach. This is a high-ownership role on a small team. You'll work directly with platform engineers and model researchers, and the integrations you build will define the boundaries of what Hark can do. Responsibilities Design and build the integration layer that connects Hark's agent to third-party services, including email, calendar, productivity tools, communication platforms, developer tools, and more. Own the full lifecycle of integrations: authentication flows, API clients, schema normalization, action execution, and error handling. Build for reliability, not just the happy path. Define and implement the tool and action protocols that let the agent interact with external systems safely and consistently. Make it easy to add new integrations without rewriting the stack. Work closely with platform engineers to integrate connectors cleanly into the agent runtime. Tool execution needs to be fast, observable, and sandboxed appropriately. Collaborate with model researchers to define tool schemas and action semantics that models can reason about reliably. The quality of the interface shapes model behavior. Instrument integrations for reliability and correctness. Track failures, surface regressions, and close the loop with the team quickly. Identify the highest-leverage integrations to prioritize, and drive the roadmap that expands what Hark can do in the world. Requirements Strong backend engineering fundamentals. You are comfortable designing APIs, handling OAuth flows, working with webhooks and rate limits, and building systems that stay reliable across third-party dependencies. Experience building integrations at scale. You've shipped connectors to real external services, and you know what breaks in production: auth token expiry, API schema drift, inconsistent error codes, flaky uptime. You build defensively. Systems thinking. You can design an integrations framework that scales to dozens of services without becoming unmaintainable. You think in abstractions, not just one-off implementations. Genuine curiosity about AI and agents. You understand how tool use works in LLM systems, you've thought about how action schemas affect model behavior, and you have opinions about what makes integrations reliable for autonomous agents. Comfort with ambiguity and fast iteration. This space moves fast. You can ship something real quickly, learn from production, and improve without waiting for perfect specs. Strong communication. You can collaborate across platform, research, and product, represent integration constraints clearly, and make sure the right tradeoffs get made. 5+ years of relevant software engineering experience. Experience building integrations or developer platforms is a strong plus. Bonus Qualifications Experience building OAuth flows, webhook infrastructure, or API gateway layers. Familiarity with integration standards and protocols such as REST, GraphQL, gRPC, and MCP. Experience designing tool-use schemas or function-calling interfaces for LLMs. Prior work on developer platforms, integration marketplaces, or iPaaS systems. Experience with execution sandboxing or permission-scoped action frameworks. Background at a fast-moving AI company or a company where integrations were core to the product. Compensation The US base salary range for this full-time position is between $170,000 - $400,000 annually. The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.