Embedded AI 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

As an Embedded AI Engineer, you will work closely with the AI research team to bring AI to Hark’s next-gen hardware. You will be responsible for the full AI stack on the device, including data ingestion, model development, optimization, and deployment on embedded devices. You should have deep understanding of the constraints of an embedded system (compute, memory, power etc) and leverage your expertise in both embedded system software development and AI model deployment to deliver production-ready ML solutions on hardware

Responsibilities

Build data collection and ingestion pipelines for an embedded system including various sensors, at scale

Work closely with model teams to co-design model architectures that meets the required latency, memory, power, and bandwidth

Work with platform vendors to bring up toolchains, SDKs and new accelerator to ensure efficient model deployment and optimization

Integrate ML inference into embedded firmware written in C, C++, or Rust

Profile and optimize memory usage, power consumption, and real-time performance

Evaluate and select silicon platforms (GPUs, NPUs etc.) for Hark’s next gen on-device and edge deployment of a wide range of models

Requirements

5 years of experience in machine learning engineering, with at least 2 years focused on embedded or edge ML

Familiarity with embedded systems, and CPU/DSP/NPU HW architectures

Hands-on experience with IMUs and other sensor types including accelerometers, gyroscopes, and microphones

Experience building sensor data collection pipelines

Familiarity and experience with embedded ML run times (e.g. TFLite, llamacpp, QNN)

Experience optimizing models for deployment on microcontrollers and edge processors such as ARM Cortex-M/A, RISC-V, and DSPs

Experience deploying workloads on NPUs or specialized accelerators for embedded systems

Bonus Qualifications

Experience with Audio/Voice/Vision models

Experience with light weight LLM models

Understand the performance characteristics of edge AI models, including CNN, RNN, transformers, KV-cache behavior, and their memory bandwidth requirements.

Experience designing hybrid edge-LLM pipelines or integrating small language models on device

Prior work on products in wearables, robotics, industrial sensing, or IoT

Compensation

The US base salary range for this full-time position is between $200,000 - $450,000 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.