Technical Lead, On-Device AI Inference

Job description

**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 own how Hark's models run on the silicon we ship: selecting the accelerators our devices are built around, co-designing architectures against real latency, memory, and power budgets, and building the low-level inference stack that turns a trained model into something that responds in milliseconds on a battery. You'll build and lead the team that does it. The ceiling on what our hardware can do is set here. **Responsibilities** - Evaluate GPUs, NPUs, DSPs, and specialized accelerators for on-device deployment, and own the recommendation hardware decisions are made against. - Work with the foundation model and audio ML teams to shape architectures that meet deployment constraints before training locks them in. - Build the low-level execution layer, custom kernels, runtime systems, and compiler paths that transformer workloads run through on target hardware. - Partner with silicon vendors and internal hardware teams to bring up new accelerators and get efficient transformer execution on them early. - Hire and lead a team of engineers on performance-critical software, and set the technical bar for the inference stack. **Requirements** - 8–12+ years in high-performance computing, including production workloads deployed on GPUs, NPUs, or specialized accelerators. - Deep understanding of attention, KV-cache behavior, quantization effects, and memory bandwidth limits. - You've designed or optimized inference engines, distributed runtimes, or ML compilers, and you write the kernels yourself when it matters. - Experience leading teams on performance-critical software. You've set direction on a stack, not just contributed to one. - You've taken a model from a research checkpoint to running on constrained hardware in a product people use. **Bonus Qualifications** - Hands-on experience with Hexagon DSP, Ambiq-class MCUs, or comparable embedded AI silicon. - Experience with speech, audio, or streaming multimodal inference where latency is perceptible to the user. - Contributions to open-source inference or compiler toolchains (TensorRT, ONNX Runtime, TVM, MLIR, and similar). **Compensation** The US base salary range for this full-time position is between $300,000 - $500,000 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.