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Edge ML Engineer– Computer Vision
Summary by AIEngineer.careers
Edge ML Engineer– Computer Vision is a Computer Vision Engineer role (full-time). with Mecka AI. in RICHMOND HILL, CA. Compensation shown: $113K–$127K (est.). Imported listing (source: earlystagestartups.com). Apply on the employer's site (earlystagestartups.com).
Imported listing (source: earlystagestartups.com) · Apply on earlystagestartups.com
RICHMOND HILL · CA
$113K–$127K (est.)
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from earlystagestartups.com
Mecka AI builds the data infrastructure layer for robotics and embodied AI, operating global systems for data capture, labeling, and hardware-enabled workflows used by leading AI labs and robotics companies. As an Edge ML Engineer focused on Computer Vision, you will train and deploy computer-vision models on embedded devices, building efficient C++ inference pipelines and optimizing for latency, memory, and power. The role may include technical leadership of model development and deployment, with direct responsibility for implementation and evaluation.
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.
Requirements
- Strong Python and C++ programming skills, including debugging and performance profiling.
- Experience training and evaluating computer-vision models with a framework such as PyTorch.
Mecka AI
Markham · CA · 65+ employees
Mecka AI is a robotics data and infrastructure company serving physical AI. It provides the data, evaluation, and deployment layer that captures, structures, and evaluates real-world human activity so robotics and embodied-AI teams can train models and move systems from research toward reliable commercial deployment.
