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Neatco Engineering Services Inc.

Machine Learning Engineer

Waterloo, ON, Canada

Contract

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Job details

Job type: Contract
Apply by: Nov 23, 2026
Posting language: English
Posted: Oct 24, 2026

Original job description

Title: Machine Learning / Computer Vision Engineer

Location: Waterloo, ON (onsite, occasional hybrid days)

Type: Full-time or part-time, 6-month to 1-year contract

About Neatco

Neatco Engineering builds AI vision and robotics systems for manufacturing and recycling. Our IntelSight platform uses deep learning and industrial imaging to run real-time quality inspection on production lines across automotive, manufacturing and recycling.

The role

You'll develop and improve the deep learning models behind IntelSight, from research ideas to models running on real factory floors. You'll work with real manufacturing datasets, industrial cameras and 3D sensors, and NVIDIA GPU and edge hardware.

This role suits people who enjoy applied research. We especially welcome master's and PhD students, postdoctoral researchers, and people who recently completed a graduate degree.

What you'll do

Train, evaluate and optimize models for detection, instance and semantic segmentation, classification and anomaly detection.
Improve IntelSight's accuracy, latency and robustness in industrial conditions.
Explore continual, incremental and few-shot learning, so new defect types need minimal retraining.
Build preprocessing, augmentation, dataset and evaluation pipelines.
Deploy models on NVIDIA GPUs and edge devices with TensorRT and ONNX.
Work with 2D cameras, 3D laser profilers, depth sensors and multispectral data.
Document experiments and benchmarks clearly.

What you bring

Strong Python and PyTorch.
Hands-on experience building computer vision models, such as detection or segmentation.
Solid grasp of training, validation, overfitting and evaluation.
Comfortable with Linux, Git and GPU environments.
Degree, or current studies, in CS, engineering, mechatronics or a related field.

Nice to have

Detectron2, YOLO, U-Net or vision transformers.
Continual learning, domain adaptation, self-supervised learning or anomaly detection.
CUDA, TensorRT, ONNX Runtime or Jetson.
3D point clouds, depth maps or 16-bit industrial images.
Publications, a thesis or open-source work in computer vision.

Details

Full-time or part-time contract for 6 months to 1 year, with potential for an extension.
Compensation is discussed at the first interview.

How to apply

Apply here: https://tally.so/r/WJ9v8R. It takes about 10 minutes and asks for your resume as a PDF.

Our process is a screening call, then an onsite technical assessment, then a final conversation with our CEO and CTO.

Neatco uses AI to help screen and assess applications. Every hiring decision is made by people.

Accommodations are available on request at any stage of the process.

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More about this job

Responsibilities

Train, evaluate, and optimize computer vision models for industrial inspection, improving accuracy, latency, and robustness while exploring continual, incremental, and few-shot learning. Build data and evaluation pipelines, deploy models to NVIDIA GPU and edge hardware, work with industrial imaging and 3D sensors, and document experiments and benchmarks.

Requirements

Applicants should have strong Python and PyTorch skills, hands-on experience building computer vision models, and a solid understanding of model training, validation, overfitting, and evaluation. A degree or current studies in computer science, engineering, mechatronics, or a related field is expected, along with comfort using Linux, Git, and GPU environments.

Skills

Python
PyTorch
Computer Vision
Deep Learning
Object Detection
Instance Segmentation
Semantic Segmentation
Image Classification
Anomaly Detection
Linux
Git
GPU Computing
Model Evaluation
Model Optimization
TensorRT
ONNX

Visa sponsorship

Not detected in the job text

Keywords

Python
PyTorch
Computer Vision
Deep Learning
IntelSight
Industrial Imaging
Manufacturing
Recycling
Robotics
Object Detection
Instance Segmentation
Semantic Segmentation
Classification
Anomaly Detection
Continual Learning
Incremental Learning
Few-Shot Learning
Data Augmentation
Dataset Pipelines
Evaluation Pipelines
NVIDIA GPUs
Edge Devices
TensorRT
ONNX
Detectron2
YOLO
U-Net
Vision Transformers
CUDA
Jetson

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