Edge ML Engineer — Embedded Computer Vision

Kindredventures; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Mecka AI is building the data infrastructure layer for robotics and embodied AI, designing global systems for data capture, labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.

You will own the work from model training and evaluation to efficient deployment on embedded devices, bringing strong computer vision, C++, and performance engineering skills to meet latency, memory, and power constraints.

Qualifications

• 5+ years of experience in machine learning or computer vision, including deploying models on embedded or resource-constrained hardware.
• Strong Python and C++ programming skills, including debugging and performance profiling.
• Experience training and evaluating computer-vision models with a framework such as PyTorch.
• Experience developing on embedded Linux.
• 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.

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.

Skills

C++

Python

PyTorch

Embedded Linux

Performance profiling

Inference optimization

Quantization

Model training

Tools

PyTorch

Job description

Mecka AI is building the data infrastructure layer for robotics and embodied AI, designing global systems for data capture, labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.

You will own the work from model training and evaluation to efficient deployment on embedded devices, bringing strong computer vision, C++, and performance engineering skills to meet latency, memory, and power constraints.