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Hardware, Edge ML Engineer
Richmond Hill, ON, Canada
Actively hiring, first seen by us 3 days ago- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: FullTime
From the employer’s posting
What you’ll work on
Full postingYou will bring intelligence to the devices that capture the world for robot learning.
You will own the work from model training and evaluation to efficient deployment on embedded devices.
Train and evaluate computer-vision models for embedded device deployment
Build efficient C++ inference pipelines and integrate them with embedded Linux
Optimize models and runtime for latency, memory, power and sustained operation
Test performance on representative data and hardware, and debug errors across the pipeline
From the employer’s posting
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.
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’ll bring
All qualificationsCore experience
- 5+ years in machine learning or computer vision, including embedded deployment
- Strong Python and C++, including debugging and performance profiling
- Experience training and evaluating vision models in PyTorch or similar
- Experience developing on embedded Linux
- Knowledge of quantization, model conversion and inference optimization on CPUs, GPUs or NPUs
Qualification wording
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.
About Mecka AI
Mecka AI is building the data infrastructure layer for robotics and embodied AI.
In the employer’s words · Read in context
Posting record
Report an errorActively hiringFirst seen by us 3 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 11, 20263 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 14, 2026
- Employer says posted
- Oct 7, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Mecka AI is hiring
4 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- C++
- Python
- PyTorch
Where the posting names them
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.
- 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.
- 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.
Your next step
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Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Richmond Hill, ON, Canada
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.