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Python Ml Developer Jobs in Montreal, QC (NOW HIRING)

Norbert Health

Applied AI Engineer

Montreal, QC · On-site

Integrate foundation models and ML components (VLMs, LLMs, ASR/TTS, detection/segmentation ... Strong Python; comfortable with the deployment toolchain (ONNX, quantization, at least one ...

Norbert Health

Applied AI Engineer

Montreal, QC

Integrate foundation models and ML components (VLMs, LLMs, ASR/TTS, detection/segmentation ... Strong Python; comfortable with the deployment toolchain (ONNX, quantization, at least one ...

PSP Investments

Senior Applied AI Engineer

Montreal, QC · On-site +1

... ML and AI-serving pipelines behind Floyd on Databricks - our primary platform for data engineering, data science and AI serving * Develop across the stack: the Floyd Python SDK and its Gradio- and ...

Flinks

Senior Data Scientist

Montreal, QC · Remote

Our stack Python and SQL Google Cloud Platform BigQuery and modern data tooling PyTorch, HuggingFace and classical ML frameworks MLflow and Kubeflow FastAPI and containerized deployment Azure DevOps ...

AlayaCare

Staff Data Developer

Montreal, QC · On-site

Stay current on industry trends in data engineering, ML, and cloud computing. * Provide mentorship ... Strong skills in DBT, SQL, Python, Snowflake, Iceberg, Spark, and AWS (ECS, S3, Aurora, RDS)

YO AI Labs

Backend Engineer - Remote

Montreal, QC · Remote

$80 - $120/hr

Strong proficiency in Python 3, Java, Rust, C++, or TypeScript . * Strong understanding of ... Familiarity with AI/ML systems is a plus but not required. * Experience with rigorous code reviews ...

Flinks

Senior Data Engineer

Montreal, QC · Remote

Build and operate the ML platform - training pipelines (Kubeflow on Vertex AI), model serving ... Our stack Python, SQL, Bash Google Cloud Platform (GCP) BigQuery and dbt Airflow (Cloud Composer ...

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All JobsPython Ml Developer JobsPython Ml Developer Jobs in Montreal, QC

Python Ml Developer information

What does a Python ML Developer do?

A Python ML Developer designs, builds, and deploys machine learning models using the Python programming language. They work with large datasets, clean and process data, select appropriate algorithms, and use libraries like TensorFlow, PyTorch, or scikit-learn to implement solutions. Their work often involves collaborating with data scientists and engineers to integrate machine learning models into applications. Additionally, they may be responsible for testing, tuning, and optimizing models to achieve the best possible performance in real-world scenarios.

What are the key skills and qualifications needed to thrive as a Python ML Developer?

To thrive as a Python ML Developer, you need strong programming skills in Python, a solid understanding of machine learning algorithms, and a background in mathematics or statistics, often supported by a degree in computer science, engineering, or a related field. Familiarity with tools and libraries such as TensorFlow, scikit-learn, PyTorch, and version control systems like Git is essential, along with experience using data visualization and cloud platforms. Critical soft skills include problem-solving, adaptability, and effective communication to collaborate with cross-functional teams and explain complex models to stakeholders. These skills ensure the successful development, deployment, and maintenance of machine learning solutions that drive business value.

What are some common challenges Python ML Developers face when deploying machine learning models to production?

Python ML Developers often encounter challenges such as ensuring model scalability, managing dependencies, and maintaining reproducibility when deploying models into production environments. Integrating machine learning models with existing systems can require close collaboration with DevOps and software engineering teams to streamline workflows and automate deployment pipelines. Additionally, monitoring model performance over time and handling data drift are crucial responsibilities to ensure continued accuracy and reliability of deployed solutions.

What is the difference between Python Ml Developer vs Data Scientist?

AspectPython Ml DeveloperData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; Python, ML certificationsBachelor's/Master's in Data Science, Statistics, or related; Python, ML certifications
Work EnvironmentSoftware development teams, AI/ML projectsResearch, data analysis, modeling teams
Employer & Industry UsageTech companies, startups, AI firmsFinance, healthcare, tech, research institutions
Common Search & ComparisonYesYes

Python ML Developers focus on building and deploying machine learning models using Python, often working closely with software engineering teams. Data Scientists analyze data, create models, and generate insights, often using Python along with statistical tools. While both roles require Python and ML knowledge, Python ML Developers are more involved in implementation and deployment, whereas Data Scientists focus on data analysis and research.

What are popular job titles related to Python Ml Developer jobs in Montreal, QC?

For Python Ml Developer jobs in Montreal, QC, the most frequently searched job titles are:

What job categories do people searching Python Ml Developer jobs in Montreal, QC look for?

The top searched job categories for Python Ml Developer jobs in Montreal, QC are:

Python Ml Developer jobs near you
Infographic showing various Python Ml Developer job openings in Montreal, QC as of August 2026, with employment types broken down into 1% Internship, 88% Full Time, 7% Part Time, and 4% Contract. Highlights an 78% Physical, 7% Hybrid, and 15% Remote job distribution.

Applied AI Engineer

Montreal, QC • On-site

Norbert Health
Fitness and Sports Centers • 11 - 50 employees
Learn more about Norbert Healthexternal

Full-time

Medical, Vision

Re-posted 15 days ago

Key responsibilities

  • Integrate foundation models and ML components into production pipelines, including open-weight models and third-party APIs.

  • Build RAG and agent-style orchestration for clinical reporting and conversational interfaces.

  • Ship real-time streaming pipelines and batch workloads, and develop evaluation harnesses to measure model performance against clinical accuracy targets.

Job description

The company

Norbert is building autonomous robots that deliver healthcare.

Our AI sensing platform enables existing robotic platforms to become care team members: rounding on patients, capturing vitals without contact (FDA-cleared for pulse and respiratory rate, more in the pipeline), running assessments, documenting to the EMR, and escalating when something's wrong. Autonomously.

We're not building demos. We're deployed in real facilities today, monitoring hundreds of patients daily. We're solving one of healthcare's hardest problems: a global nursing shortage that will hit 40% by 2030.

We're a small, international team backed by top-tier VCs, with offices in Brooklyn, Paris, and Montreal. We ship things that matter.

The position

We're looking for an Applied AI Engineer to take our growing collection of foundation models and ML components from manually run, sometimes locally trained workflows to fully automated, production-grade MLOps pipelines: deployed reliably on robots in nursing facilities.  We need someone who knows the model landscape cold, treats evaluation as a first-class engineering problem, and has strong opinions about when to prompt, RAG, fine-tune, swap, or buy.

You'll work across cloud and edge deployments, and some of the systems you'll touch are on a SaMD pathway, so you'll need to be comfortable shipping under regulatory constraints.

What you'll do
  • Integrate foundation models and ML components (VLMs, LLMs, ASR/TTS, detection/segmentation, embeddings) into our production pipelines, using both open-weight models and third-party APIs
  • Build RAG and agent-style orchestration for clinical reporting and conversational interfaces
  • Ship real-time streaming pipelines (voice agents) alongside batch and request-response workloads
  • Build evaluation harnesses that catch regressions across model swaps and measure performance against clinical-grade accuracy targets
  • Fine-tune and retrain models (LoRA, PEFT, supervised fine-tuning) using data collected from our deployed fleet
  • Deploy across our inference surfaces: third-party APIs, self-hosted, and on-robot edge
  • Build the data flywheel: pipelines that collect, label, version, and feed production data back into model improvement
  • Partner with the algorithms team (signal processing, computer vision) on integration with their lower-level pipelines
What we're looking for
  • BS in Computer Science, Engineering, or a related field, or equivalent hands-on experience
  • 4+ years shipping ML/AI systems in production outside of academic settings
  • Strong working knowledge of the modern foundation model landscape (open-weight LLMs and VLMs, common detection/segmentation backbones, embedding models)
  • Hands-on experience with PEFT/LoRA and supervised fine-tuning
  • Strong Python; comfortable with the deployment toolchain (ONNX, quantization, at least one inference runtimeTensorRT, vLLM, llama.cpp, etc.)
  • Experience with a cloud ML training/MLOps platform (GCP Vertex AI, AWS SageMaker, Azure ML, or equivalent)
  • Ability to work independently, solve complex problems, and drive projects to completion
Bonus points
  • Edge ML deployment (Jetson, ARM, mobile NPUs)
  • Real-time voice AI pipelines (STT, TTS, streaming LLM)
  • Production RAG systems beyond toy implementations
  • Medical devices, SaMD, or other regulated ML environments
  • MLOps tooling (Weights & Biases, MLflow, DVC, etc.)
  • Active learning or human-in-the-loop labeling workflows
  • C++ for integrating with our computer vision pipeline
What we offer
  • Real impact: your code provides care for patients today
  • High autonomy and technical ownershipyou'll define how we operate AI in production
  • Work at the intersection of cutting-edge AI, edge computing, and healthcare
  • A talented, excellent, diverse and international team
  • Equity participation in the company's future
  • Cutting-edge stack: embedded AI, robotics, LLMs, multimodal sensing
  • Transparent, mission-driven culture focused on continuous learning
  • Competitive salary and equity