Staff Machine Learning Platform Engineer

Faire; Toronto; On-site; CAD 90,000 - 120,000; Full time

Job summary

Deepstreamtech is seeking an experienced professional to design, improve, and operate a scalable ML platform in Toronto, Ontario. The ideal candidate will have over 8 years of experience in building production ML platforms and a strong understanding of MLOps best practices.

The role involves serving as the technical bridge between data science and production engineering, optimizing performance, reliability, and cost across training and inference workloads.

Qualifications

• 8+ years of experience building production ML or data platforms.
• Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow.
• Experience supporting multiple ML teams in a shared platform environment.

Responsibilities

• Design, improve, and operate a scalable ML platform.
• Serve as the technical bridge between data science and production engineering.
• Productionize ML workloads using Spark, Delta Lake, MLflow, and Databricks Workflows.

Skills

Databricks

Spark

Delta Lake

MLflow

Python

SQL

Distributed systems concepts

MLOps best practices

Terraform

Cloud platforms

Education

Degree in Computer Science, Engineering, or Statistics

Tools

AWS

Kubernetes

Docker

GitHub Actions

Airflow

Job description

Requirements

• 8+ years of experience building production ML or data platforms
• A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field
• Strong hands‑on expertise with Databricks, Spark, Delta Lake, and MLflow
• Proficiency in Python, SQL, and distributed systems concepts
• Experience with cloud platforms and infrastructure‑as‑code
• Solid understanding of MLOps best practices: CI/CD, monitoring, reproducibility, and security
• Experience supporting multiple ML teams in a shared platform environment
• Active ownership of orphaned problems and a willingness to assimilate missing knowledge to get the job done

What the job involves

• Design, improve, and operate a scalable ML platform to accelerate model training, deployment, and governance
• Serve as the technical bridge between data science and production engineering
• Join a small, critical team that scales Faire’s ability to support tens of thousands of local businesses in a constantly narrowing retail landscape
• Design and operate ML infrastructure, including workspaces, clusters, jobs, and workflows
• Productionize ML workloads using Spark, Delta Lake, MLflow, and Databricks Workflows
• Teach data scientists how to utilize the ML platform to advance development from notebook to production for our most critical models
• Implement Unity Catalog for data governance, lineage, access control, and secure multi‑tenant usage
• Build CI/CD pipelines for ML using Terraform and Git‑based workflows (e.g., GitHub Actions)
• Optimize performance, reliability, and cost across training and inference workloads
• Configure IAM and RBAC for sensitive data sets
• Establish observability for data quality, model performance, and platform health
• Build and maintain ML Platform technical documentation

Tech Stack

• Languages: Python, SQL, Kotlin
• ML Frameworks: PyTorch, MLFlow
• Big Data & Processing: Spark, Kafka, Databricks, Snowflake, Fivetran, Iceberg, Unity Catalog, Datadog, Airflow, Cockroach DB, MySQL
• Cloud & Infrastructure: AWS, S3, SageMaker, Kubernetes, Docker, GitHub Actions, Terraform
• Generative AI: Claude Sonnet 4.5, ChatGPT 5.2