Perception Engineer

Human Computer Lab is a research lab building character robots that feel alive and responsive. Our first robot, LeLamp, explores a new category of consumer robotics where everyday objects become interactive and help us reshape our attachment to technology.

Our goal is to push the frontier of human-robot interaction by making technology more legible, emotionally intuitive, and human-centered. We are building the foundation for a new generation of robots designed for everyday environments.

What to Expect

We are looking for a hands-on Perception Engineer who can build the system that allows our robot to understand people and the world around it. You will work on developing computer vision and machine learning system at the core of the robot’s awareness, enabling it to recognize people, understand gestures, track attention, and respond naturally in real time. The liveliness of our robot will truly convey the character interface that we are building that can learn and adapt as required.

This role sits at the intersection of robotics, machine learning, and software engineering. You will work closely with the CEO and the founding team to develop perception capabilities from prototype to deployment, collecting data, training models, optimizing performance, and integrating the stack directly into the robot's behavior.

In this Role, You Will:

• Design, train, and deploy computer vision and perception models for person detection, tracking, pose estimation, gesture recognition, emotion recognition and scene understanding.

• Develop data collection, labeling, and evaluation pipelines to improve model performance and reliability.

• Build perception systems that operate robustly across varying lighting conditions, environments, and user behaviors.

• Optimize models for real-time inference on edge hardware while balancing latency, accuracy, and power constraints.

• Work with RGB cameras, depth sensors, IMUs, audio inputs, and other sensor modalities to create multi-modal perception systems.

• Develop tools for dataset management, model evaluation, debugging, and monitoring.

• Support integration between perception, behavior, controls, and firmware systems.

• Stay updated with state-of-the-art research to develop architectures and techniques that can improve the robot’s ability to understand and interact with people.

• Debug perception failures systematically and close the loop through data-driven improvements.