Anuar Yeraliyev, Machine Learning Developer in Toronto, Canada
Anuar Yeraliyev

Machine Learning Developer in Toronto, Canada

Member since May 6, 2022
Anuar is a machine learning engineer with more than four years of experience. He successfully brought state-of-the-art machine learning and deep learning models from ideas or research papers to production to customers. One of his areas of expertise is applied computer vision on edge devices. Anuar has experience in full-stack web development and aims to provide full-stack and customer-centric machine learning solutions.
Anuar is now available for hire




Toronto, Canada



Preferred Environment

Linux, MacOS, Slack, Email, Discord, Loom, Zoom, Git

The most amazing...

...thing I've contributed to is a video-to-text translation tool for sign language, applying deep learning and machine learning methods.


  • Machine Learning Engineer

    2022 - PRESENT
    Layer 6 (TD Bank)
    • Created data pipelines, productionization, and scaling ML models for TD, one of the largest banks in North America and the largest bank in Canada. Layer 6 is a leading research and applied AI branch of TD.
    • Built development and production data pipelines on large customer datasets for various financial use cases using Spark, Databricks notebooks, and Azure.
    • Involved in hands-on model development that includes feature engineering, model training, and inference.
    Technologies: Java, Scala, Spark, Databricks, Azure
  • Technical Co-founder

    2020 - 2021
    Anooka Health
    • Developed a MERN-based web application from scratch. Conducted user interviews, designed a prototype, developed an initial MVP and final product, and managed two full-stack engineers and the development of the product.
    • Performed in-depth research of 3D pose estimation and its applications in fitness, such as form feedback and rep counting, and designed the system that operates on the edge and in the cloud.
    • Developed three MERN web apps—beta version for initial testing with users in five weeks, the final version as direct reports with two SWs and designer in three months, and a video-based partner exercising app in six weeks.
    Technologies: JavaScript, Node.js, React, Computer Vision
  • Machine Learning Engineer

    2019 - 2020
    Passenger AI
    • Built an online service to detect objects during cabin surveillance on AWS that was 30% more accurate yet as effective as the previous model for Passenger AI, a VC-backed startup revolutionizing safety monitoring for self-driving vehicles.
    • Obtained incredible results (F1 score > 0.95) on action recognition that could run in real time on low-power NVIDIA Jetson Nano.
    • Profiled and optimized concurrent on-device client code to efficiently execute business logic and neural network inference.
    • Learned and experimented with computer vision topics like multi-view geometry, tracking, person re-identification, optical flow, and gaze estimation to understand how this could influence product in the short and medium-term.
    • Researched optics, camera sensors, and lenses to understand how cameras could drive products. Proactively drove the transition to a new, more robust camera system that performed better across difficult imaging conditions, such as low light.
    Technologies: Python, Deep Learning, Computer Vision, AWS, Machine Learning, Machine Learning Operations (MLOps), Kubernetes
  • Machine Learning Engineer

    2018 - 2019
    Motion Metrics
    • Developed a new deep learning architecture and real-time computer vision pipeline running on constrained edge devices that resulted in 3x improvements in business metrics and new features for the company's main cash cow product.
    • Researched state-of-the-art methods for object detection, pose estimation, and action recognition and performed system design of full computer vision pipeline. One part of the contribution was published in the CVPR workshop.
    • Contributed to the ML lifecycle, including research, prototyping and experimentation, data operations, evaluation, model optimization, and deployment.
    Technologies: Python, C++, Computer Vision, TensorFlow, PyTorch, Machine Learning, Machine Learning Operations (MLOps)


  • Video-to-Text Sign Language (ASL) Translation

    The goal was to translate sign language performed by humans from a video to text that non-signers could then understand. I collected and labeled my dataset, designed 3D convolutional sequence-to-sequence architecture, and trained the model on AWS.


  • Languages

    Python, JavaScript, C++, Java, SQL, Scala
  • Libraries/APIs

    TensorFlow, PyTorch, Scikit-learn, Node.js, React
  • Other

    Machine Learning, Deep Learning, Computer Vision, Machine Learning Operations (MLOps), Email, Discord, Loom, Economics, Research, Distributed Systems, AWS, System Design, Recommendation Systems
  • Frameworks

  • Tools

    Slack, Zoom, Git
  • Paradigms

    Object-oriented Programming (OOP)
  • Platforms

    Linux, MacOS, Kubernetes, Databricks, Azure
  • Storage

    MySQL, MongoDB


  • Bachelor's Degree in Computer Science and Physics with minor in Economics
    2013 - 2018
    University of British Columbia - Vancouver, BC, Canada
  • Exchange and Research Program in Computer Science
    2017 - 2017
    ETH Zurich - Zurich, Switzerland


  • Machine Learning System Design
    APRIL 2022 - PRESENT

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