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Digital Nomad World

Senior Staff Machine Learning Platform Engineer

ON, Canada
Ontario, Canada, Canada

Canada: the pay range for this role is $248,000 to $341,000 per year. · Full-time

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Job details

Pay: Canada: the pay range for this role is $248,000 to $341,000 per year.
Job type: Full-time
Apply by: Nov 4, 2026
Experience: 10-12 years of experience building and improving large-scale ML or data platforms.
Posting language: English
Posted: Oct 5, 2026

Original job description

Kubernetes Machine Learning Python

Remote eligibility: Ontario, Canada.

About Faire

Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we’re using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town - we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive.

We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours.

Note: This role can also be performed remotely for candidates located in Ontario, Canada.

About This Role

As the Senior Staff Machine Learning Platform Engineer, you will own the technical vision and evolution of Faire’s ML platform. You will set standards, influence org-wide architecture, and lead complex, cross-functional initiatives that unlock data science velocity at scale. This role will also be key to adapting ML workflows to take advantage of modern AI productivity tools. You won’t just build models, you will architect the systems that allow those models to help tens of thousands of small retailers compete and grow their local businesses.

What You Will Do

Define and drive the long-term architecture of Faire’s ML platform including training, inference, feature management, governance
Establish company-wide standards for code quality, testing, MLOps (CI/CD), experimentation, model lifecycle management, and observability
Lead adoption and advanced use of Unity Catalog, multi-workspace strategies, and data/ML mesh patterns
Architect highly scalable ML workflows using Spark, Delta Lake, and MLflow
Optimize performance, reliability, and cost of the ML platform
Evaluate and integrate emerging Databricks features
Stay ahead of the curve by engaging with the latest developments in machine learning and AI
Serve as senior ML technical advisor to Faire’s data science and production engineering teams
Represent Faire at ML conferences and meetups
Mentor ML engineers and raise the overall bar for Machine Learning at Faire

What It Takes

10-12 years of experience building and improving large-scale ML or data platforms.
A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field.
Deep expertise in Databricks lakehouse architecture, including governance via Unity catalog, orchestration via Workflows, and cost optimization
Proven ability to design systems that support multiple data science teams and production workloads
Strong background in distributed systems, ML infrastructure, and cloud architecture.
Demonstrated technical leadership across teams and orgs; ability to influence without authority
Experience integrating LLM workflows into enterprise platforms is a plus
Previous contributions to open source ML Infrastructure projects or research publications is a very strong plus

Tech Stack

Faire uses a modern cloud based tech stack. For this role, you’ll want to be proficient with the following:

Category

Technologies

Languages

Python, SQL, Kotlin

ML Frameworks

PyTorch, PySpark, 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

Salary Range

Canada: the pay range for this role is $248,000 to $341,000 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting.

Why you’ll love working at Faire

Move fast: You’ll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.
Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day.
Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours.
Real rewards. Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work.
Belonging: We’re intentional about building an environment where every Faire employee has equal access to opportunities, growth, and success.

Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our blog.

Faire provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity or gender expression.

Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs. To request reasonable accommodation, please fill out our Accommodation Request Form (https://bit.ly/faire-form)

Privacy

For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s Privacy Notice (https://www.faire.com/privacy)

Originally listed on We Work Remotely.

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More about this job

Responsibilities

Define and evolve the architecture and standards for Faire’s machine learning platform, including training, inference, governance, MLOps, experimentation, and observability. Lead cross-functional platform initiatives, optimize scalability, reliability, and cost, advise data science and production engineering teams, and mentor ML engineers.

Requirements

Requires 10–12 years of experience building or improving large-scale machine learning or data platforms, along with deep expertise in Databricks lakehouse architecture, Unity Catalog, Workflows, and cost optimization. A degree in computer science, engineering, statistics, or a related technical field is preferred, and candidates should bring strong distributed systems and cloud architecture experience plus demonstrated technical leadership.

Skills

Machine Learning Platform Architecture
Databricks
Unity Catalog
MLOps
Distributed Systems
Cloud Architecture
Python
SQL
Kotlin
PyTorch
PySpark
MLflow
Apache Spark
Delta Lake
Kubernetes
Machine Learning Governance

Benefits

Equity
Comprehensive Benefits

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Machine Learning Platform
Databricks
Lakehouse Architecture
Unity Catalog
Databricks Workflows
MLOps
CI/CD
Spark
Delta Lake
MLflow
Python
SQL
Kotlin
PyTorch
PySpark
Kafka
Snowflake
Fivetran
Apache Iceberg
Datadog
Airflow
CockroachDB
MySQL
AWS
Amazon S3
SageMaker
Kubernetes
Docker
Terraform

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