Data Scientist / Machine Learning Engineer (Recommendation Systems)

Beatdapp; Vancouver; On-site; CAD 100,000 - 130,000; Full time

Job summary

An innovative technology startup in Vancouver is seeking a Data Scientist / Machine Learning Engineer specializing in Recommendation Systems. This role involves designing scalable architectures, developing deep learning models for rich user interaction, and ensuring models perform in a production environment. The ideal candidate has over 3 years of experience in machine learning, proficiency in Python, and a solid understanding of deep learning techniques. The company offers an opportunity to work on cutting-edge technology in a dynamic team environment.

Qualifications

• 3+ years of experience in Data Science or Machine Learning Engineering with a focus on Recommendation Systems.
• Proficiency in Python and ML frameworks like PyTorch and TensorFlow.
• Practical knowledge of advanced ML techniques relevant to recommendation systems.
• Familiarity with vector databases and cloud infrastructure.
• Solid understanding of advanced statistical concepts.

Responsibilities

• Design and implement scalable recommendation architectures.
• Own projects across the Machine Learning lifecycle.
• Develop deep learning models to create user and item embeddings.
• Execute distributed training workflows using multi-GPU strategies.
• Mine high-dimensional user and track event data to enhance models.

Skills

Recommendation Systems

Python

Deep Learning

Machine Learning

Data Engineering

Tools

PyTorch

TensorFlow

GCP

AWS

Job description

About Beatdapp

Beatdapp is a venture-backed startup delivering the most advanced streaming integrity and recommendation technology in the world1. While our roots are in fighting the multi-billion dollar problem of streaming fraud, we have leveraged our "Trust & Safety Operating System" to power a new generation of discovery.

We believe that true personalization starts with verified behavior. By filtering out noise and manipulated signals before they impact the model, we build recommendation engines on a foundation of clean, authentic data. We are looking for builders who want to work with the world’s best streaming services and music labels to reshape how content is discovered.

The Role

We are seeking a Data Scientist / Machine Learning Engineer who specializes in Recommendation Systems. You will move beyond simple analysis to research, build, and deploy production-grade models that power discovery for millions of users.

In this role, you will bridge the gap between research and engineering. You will design advanced architectures and ensure they run efficiently at scale. You will work closely with our leadership and data engineering teams to turn trillions of data points into experiences that anticipate user intent in real-time.

Responsibilities

• Build End-to-End Recommendation Pipelines: Design and implement scalable recommendation architectures to surface relevant content from large catalogs.
• Full-Cycle Project Ownership: Take ownership of projects across the complete Machine Learning lifecycle, driving initiatives from initial problem formulation and exploratory analysis to model training, validation, and post-deployment monitoring.
• Advanced Behavioral Modeling: Develop and train deep learning models (e.g., GNNs, Transformers, Wide-to-Narrow networks) to create rich user and item embeddings based on authentic interactions.
• Scalable Multi-GPU Training: Design and execute distributed training workflows for large-scale deep learning models, utilizing multi-GPU strategies and parallel computing techniques to maximize training throughput and handle massive datasets efficiently.
• Strategic Signal Extraction & Feature Modeling: Systematically mine and sift through high-dimensional user, track, and streaming event data to distinguish between subtle implicit signals and explicit feedback, mathematically modeling these behaviors to engineer dense, predictive features that enhance model performance.
• Production Engineering: Write clean, production-ready code (Python) and oversee the deployment of models into high-availability environments. You will optimize models for low latency to ensure instant load times.
• Cross-Functional Collaboration: Partner closely with Product and Engineering teams to translate business requirements into technical specifications, ensuring seamless development, integration, and deployment of models into the core product ecosystem.

Successful Candidates will have

• 3+ years of experience in Data Science or Machine Learning Engineering, with a specific focus on building and deploying Recommendation Systems in production environments.
• Strong Engineering Chops: Proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow).You are comfortable writing production-grade code that can handle large-scale data, not just notebooks.
• Deep Learning Expertise: Practical knowledge of modern ML techniques relevant to RecSys, such as Deep Clustering, Graph Neural Networks (GNNs), Transformers, and Representation Learning.
• Architecture Experience: Familiarity with vector databases, embedding spaces, and cloud infrastructure (GCP/AWS) required to support high-velocity data ingestion and real-time inference.
• Mathematical Foundation: Solid understanding of advanced statistical concepts, matrix factorization, and probability distributions.
• Product-First Mindset: A drive to solve complex product problems—such as "churn reduction" or "session continuity"—using data, rather than just optimizing theoretical metrics.

Bonus Points

• Experience in the media, music, or video streaming domains.
• Experience with "Agentic AI" or semantic search technologies.
• Knowledge of fraud detection or anomaly detection within recommender loops.