Full Stack Engineer, MLOps

Toronto, ON /
Engineering /
Full-time
We are hiring a full-stack engineer for MLOps, a hot new field combining full-stack engineering, DevOps, and machine learning. This is an opportunity to take your career to the next level by automatically retraining and deploying machine learning models, managing data collection & annotation, and measuring model performance.

In this role, you’ll greatly impact our key metrics by using data to improve model performance. You’ll get exposure to state-of-the-art machine learning technology, and you’ll be part of a collaborative team that celebrates mentorship and training.

Only 22% of companies that build machine learning models successfully deploy them. MLOps is a hot new space projected to grow from $350M in 2020 to $4B in 2025. You can read more about this exciting space full of growth opportunities here: https://ml-ops.org/content/motivation

About Zippin
Zippin provides autonomous checkout for leading brick & mortar retailers. Shoppers just walk into stores, pick up the items they want, and then leave. We use machine learning, computer vision, and sensor fusion to automatically charge shoppers when they walk out—no more checkout lines.

Zippin’s headquarters are in San Francisco, CA, with offices in Toronto, CA, and India. Industry veterans from Amazon, Apple, VMWare, and SRI founded the company and raised over $15M in Series A funding from Evolv, NTT Docomo, SAP, and Maven Ventures.

Zippin is growing fast and is scaling to meet customer demand. Our Toronto, CA office is brand new and is hiring for this role and others.

Responsibilities

    • Creating frontend applications and their APIs for data annotation, data collection, and reporting so that our team can scale data operations
    • Working directly with both the ML and operations teams to understand and serve their data needs
    • Improving existing application tooling to be more rapidly deployable so we can serve more customers
    • Fulfilling and productizing requests from the ML team for automated data operations

Qualifications

    • Balanced experience in and preference for frontend and backend with a skew toward frontend
    • Proficiency with JavaScript, SQL, and Python
    • Familiarity with one or more of AWS, Google Cloud, or Azure
    • 1-3 years of work experience in full-stack development
    • Exposure to automated testing and CI/CD

Nice-to-Haves

    • Working knowledge of machine learning engineering nice-to-have but not required
    • Understanding of fundamental ML concepts like metrics, biases, and datasets
    • Knowledge of Kubernetes, Docker, or other container orchestration systems
    • Background building and documenting developer tooling
    • Experience working with and supporting other developers by creating tools for them

Technologies we use

    • Languages: JavaScript, Python, C++, HTML, CSS
    • Frameworks & Platforms: React, Node.js, TensorFlow, Docker, Google Cloud, Azure
    • Data & Databases: MySQL, Redis, Google Cloud Buckets
    • Machine Learning: TensorFlow, TensorFlow-Lite
    • Machine Learning Platforms: Google Cloud AI Platform Pipelines, Azure Machine Learning MLOps
    • Machine Learning Hardware: NVIDIA Jetson, Google Coral
    • Testing: Jest, PyTest, Jenkins, Bitbucket Pipelines
    • Google Cloud Services: Kubernetes Engine, Compute Engine, Cloud SQL, PubSub

Perks & benefits

    • Full benefits including medical, dental, and vision
    • Flexible working hours and work-from-home policy
    • Unlimited vacation time
    • Free coffee and snacks
    • Standing desks
    • Office located at Queen St W & Bathurst St in a quiet pod within a coworking space