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Machine Learning Product Engineer Jobs in Quebec

Jumio

Senior Machine Learning Engineer

Montreal, QC

We're looking for a Senior Machine Learning Engineer with strong computer vision expertise to join ... Production Engineering: Optimize models for low-latency inference (quantization, distillation ...

LawZero

Machine Learning Manager

Montreal, QC · On-site

You will bridge the gap between cutting-edge scientific theory and large-scale engineering ... Display managerial courage as needed to continue fostering a healthy and productive work ...

Eli Health

Machine Learning Scientist

Montreal, QC · On-site

Eli's flagship product, Hormometer ® , is the first instant hormone monitoring platform to deliver ... Perform feature engineering, model selection, validation, and error analysis. * Identify issues ...

Unitytech

Senior Machine Learning Engineer

Montreal, QC

CA$147K - CA$220K/yr

Integrate Machine Learning models seamlessly within real-time game engine constraints, optimizing ... production, ideally within real-time or interactive environments. * Strong background in one or ...

YO AI Labs

Machine Learning Engineer - Remote

Montreal, QC · Remote

$80 - $120/hr

Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software ...

Td

Applied Machine Learning Scientist II

Montreal, QC

CA$125K - CA$154K/yr

Experience with structured and unstructured data, feature engineering, and model interpretability ... Every day, we strive to make every interaction, product, and experience remarkably human and ...

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Machine Learning Product Engineer information

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Machine Learning Engineer - (Computer Vision)

Montreal, QC

Jumio
Software Development • 1 - 5K employees
Learn more about Jumioexternal

Full-time

Re-posted 22 days ago

Job description

Machine Learning Engineer - (Computer Vision)

We're looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You'll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process.

What You'll Do
  • Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition)
  • Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions.
  • Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX
  • Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps.
  • Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS.
  • Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.
What We're Looking For
  • Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis.
  • Deep expertise in computer vision and biometrics, especially face recognition.
  • Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact.
  • Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code.
  • Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow.
  • Cloud Native: Hands-on experience scaling training jobs on multi-GPU clusters and deploying services on AWS (SageMaker, EC2, EKS).
Nice to Have
  • Research Publications: Papers in CVPR, ICCV, ECCV, or FG related to face recognition, image quality assessment, or fairness.
  • Large Scale Search: Experience with vector databases (e.g., Milvus, Faiss) and approximate nearest neighbor (ANN) search algorithms.
  • Familiarity with privacy, security, and compliance in biometric systems.
  • Mobile/Edge Experience: Experience porting models to edge or mobile devices utilizing frameworks such as CoreML, LiteRT, and/or TFLite.
  • Synthetic Data: Experience using GANs or diffusion models to generate synthetic faces for training.
  • Strong communication skills.

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