We are a global media and tech company that connects people to their passions. We reach nearly 900M people around the world, bringing them closer to what they love—from finance and sports to shopping, gaming and news—with the trusted products, content, and tech that fuel their day. About the Role

We are looking for a Senior Machine Learning Engineer to design and scale personalization systems that power contextual ad rendering and recommendation experiences.

This role focuses on building production-grade, low-latency ML systems that leverage user signals and smart insights to improve relevance, engagement, and yield—while maintaining strong privacy, security, and compliance standards. What You’ll Do

Design and implement scalable end-to-end ML systems and infrastructure for personalization and ranking use cases

Build and optimize classification, ranking, and contextual models

Develop and productionize models with ownership across the full ML lifecycle (training, evaluation, deployment, monitoring)

Build and maintain ML pipelines, feature stores, and model monitoring systems

Optimize ML systems for low-latency, high-availability production environments

Improve model and system performance (latency, throughput, quantization, pruning, system bottlenecks)

Develop privacy-safe data pipelines and ensure secure handling of sensitive user signals

Support experimentation frameworks (A/B testing) to improve business metrics such as engagement and yield





Partner with cross-functional teams to deliver reliable, scalable ML solutions at scale Required Qualifications

6+ years of experience in Machine Learning Engineering or ML Systems Engineering

Recent hands-on experience designing and deploying scalable ML systems (within last 12 months)

Strong experience with

-Machine learning algorithms and statistical modeling

-Recommendation systems, ranking, or contextual personalization

-End-to-end model lifecycle management in production

-ML infrastructure, pipelines, and monitoring

Experience optimizing ML inference for performance and scale

Strong programming skills in Python (Java is a strong plus)

Hands-on experience with TensorFlow or PyTorch

Experience with distributed systems, streaming architectures, or high-availability microservices

Experience with containerization and orchestration tools (Docker, Kubernetes)

Knowledge of CI/CD and infrastructure tools Nice to Have

GCP ML Tech stack

Experience with IaC

Experience in AdTech, large-scale consumer platforms, or marketplace systems

Experience operating ML systems in PII-heavy or regulated environments

Familiarity with privacy-preserving ML techniques (tokenization, anonymization, differential privacy)

Experience optimizing models for revenue, CTR, or conversion metrics Engagement Highlights:

-Commitment: Full time (40 hr/week) preferred -Client will provide a laptop for this engagement -Required Overlap: 6 hours with PST

📌 Machine Learning Engineer (Canada)
🏢 Toptal
📍 Canada