Hardware, Edge ML Engineer

Department: Hardware Engineering- Theo

Location: Toronto GTA, New York, San Francisco

Compensation: CA$170K – CA$190K • Offers Equity

Employment Type: FullTime

About Mecka AI

Mecka AI is building the data infrastructure layer for robotics and embodied AI.
We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.

About the Role

You will bring intelligence to the devices that capture the world for robot learning. As Mecka's capture network grows, your models will turn advanced computer vision into capabilities that work wherever people collect data, within the limits of real hardware.

You will own the work from model training and evaluation to efficient deployment on embedded devices. Bring strong computer vision, C++, and performance engineering skills to the challenge of delivering accurate results under tight latency, memory, and power constraints.

What You'll Do

• 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.

What You Bring

Must-have:

• 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.

Nice-to-have:

• Experience with accelerator runtimes, custom operators, or camera and video pipelines.

• Experience with dataset curation.

About You

• You are ambitious about what you build. You can point to results you personally delivered.

• You care deeply about your craft, pursue difficult questions, and learn quickly.

• You take initiative and own problems through to results. You work independently, make decisions, and bring others in when needed.

• You communicate honestly. You own mistakes, share credit, and help the people around you succeed.

If you don't check every box above but believe you can do the job, we encourage you to apply — we're looking for capability and trajectory, not a perfect checklist match.

We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, we will partner with you to meet your needs.

We may use AI-assisted tools to help evaluate applications against the requirements of this role. These tools support our recruiting team in identifying qualified candidates but do not replace human judgment or limit which applications may be reviewed or considered.