Embedded Computer Vision Engineer for Robotics

Kindredventures; Toronto; On-site; CAD 90,000 - 130,000; Full time

Job summary

Mecka AI is building the data infrastructure layer for robotics and embodied AI. You will put advanced computer vision into hardware that captures experience for robot learning. You will train models, run them on embedded devices, and balance latency, memory, and power to maintain accuracy in real-world conditions.

This role offers hands-on implementation and, for the lead position, architecture decisions and standards setting across model development and deployment pipelines.

Qualifications

• Strong Python and C++ programming skills with debugging and performance profiling.
• Experience training and evaluating computer-vision models with PyTorch.
• Experience developing on embedded Linux and deploying models on resource-constrained hardware.
• Understanding of quantization, model conversion, and inference optimization on CPUs, GPUs, or NPUs.
• Ability to evaluate accuracy, false positives, and missed detections across changing lighting, motion, and operating conditions.
• For the lead position: experience making architecture decisions and guiding engineers through model development and deployment.

Responsibilities

• Train and evaluate computer-vision models for deployment on embedded devices.
• Build efficient C++ inference pipelines and integrate them with embedded Linux software.
• Optimize models and runtime performance for latency, memory, power consumption, and sustained operation.
• Test performance on representative data and hardware, and investigate errors across preprocessing, inference, and integration.
• For the lead position: set architecture and evaluation standards while continuing to develop models and software.

Skills

Python

C++

PyTorch

Embedded Linux

Quantization

Inference optimization

Model evaluation

Tools

Accelerator runtimes

Custom operators

Camera pipelines

Dataset curation

Job description

Mecka AI is building the data infrastructure layer for robotics and embodied AI. You will put advanced computer vision into hardware that captures experience for robot learning. You will train models, run them on embedded devices, and balance latency, memory, and power to maintain accuracy in real-world conditions.

This role offers hands-on implementation and, for the lead position, architecture decisions and standards setting across model development and deployment pipelines.