Hark is an AI company building advanced personal intelligence through bespoke hardware devices and agentic systems that see, hear, remember, and act on your behalf.
<p><strong>About Hark</strong></p> <p>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.</p> <p>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.</p> <p>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.</p> <p><strong>About the Role</strong></p> <p>You'll build the platform that powers Hark's AI agent — the runtime, the infrastructure, the primitives. Everything that makes the intelligence actually works in production.</p> <p>That means owning the hard systems problems, such as low-latency streaming, reliable tool execution, memory and context at scale, and orchestration logic that holds up under real usage. You will be building the foundation that everything else runs on.</p> <p>This is a high-ownership role on a small team. You'll work directly with the people building the models and the people shipping the product, and the systems you build will define what's possible for both.</p> <p><strong>Responsibilities</strong></p> <ul> <li>Build the core runtime that powers Hark's agent platform. Orchestration, tool execution, memory retrieval, streaming, and the systems that make all of it work reliably together.</li> <li>Own features end-to-end, from early prototyping alongside model researchers to production deployment and iteration based on real usage.</li> <li>Architect the platform primitives that the rest of the product team builds on. Agent runtimes, execution sandboxes, memory APIs, streaming pipelines.</li> <li>Instrument and evaluate the product. Write evals, track quality regressions, and feed signals back into the model and product loop.</li> <li>Work across the stack when the problem requires it.</li> </ul> <p><strong>Requirements</strong></p> <ul> <li>Strong systems engineering fundamentals. You are comfortable designing APIs, building reliable infrastructure, and reasoning about performance, concurrency, and failure modes.</li> <li>Experience building production systems that real users depend on. You know the difference between something that works in a demo and something that works at scale.</li> <li>Genuine curiosity about AI and agents. You have built with LLMs, thought about how they fail, and have opinions about what makes agentic systems reliable.</li> <li>Comfort with ambiguity and fast iteration. You can start with a rough problem, build something real, learn from it, and improve quickly.</li> <li>Strong communication. You can explain tradeoffs clearly, collaborate across teams, and make good decisions with incomplete information.</li> <li>5+ years of relevant software engineering experience. Experience at a fast-growing AI or infrastructure-focused company is a strong plus.</li> <li>Bonus: hands-on experience with agent frameworks, streaming architectures, execution sandboxes, memory systems, or tool integration protocols.</li> </ul> <p><strong>Bonus Qualifications</strong></p> <ul> <li>Experience at a fast-growing AI or infrastructure-focused company.</li> <li>Hands-on experience with agent frameworks.</li> <li>Experience with streaming architectures.</li> <li>Experience with execution sandboxes.</li> <li>Experience with memory systems or tool integration protocols.</li> </ul> <p><strong>Compensation</strong></p> <p><span data-sheets-root="1">The US base salary range for this full-time position is between $170,000 - $400,000 annually.</span></p> <p>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.</p>