greenhouse
Senior Platform Engineer, Machine Learning
Movable Ink
Toronto, CAN
Full-time
Senior · 5+ years experience
Hybrid
CAD 143000-200000 yearly / year
Discovered 3 weeks ago
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Free
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Responsibilities
- Lead the architectural evolution of the core recommendations pipeline to a modern distributed architecture, delivering incremental value while maintaining production stability
- Design, build, and operate scalable, fault-tolerant backend services that power personalization at scale across content selection, send-time optimization, subject line personalization, and frequency capping
- Identify and resolve performance bottlenecks, reliability risks, and scaling limitations across the system
- Collaborate with ML engineers and product stakeholders to translate requirements into rigorous technical designs with a clear path to production
- Own key system components end-to-end - from architecture and implementation to deployment, monitoring, and incident response
- Champion engineering best practices around system design, code quality, testing, observability, and operational excellence
- Mentor engineers and contribute to a culture of technical rigor, ownership, and continuous improvement
Qualifications
- 5+ years software engineering experience with a focus on distributed systems
- Deep experience building and operating large-scale distributed systems that are fault tolerant, highly available, and highly concurrent
- Exemplary software engineering skills (system design, unit testing, git, code review, CI/CD)
- Strong foundation in Python
- Experience with distributed data processing frameworks (e.g. Apache Spark, Apache Flink, Apache Beam); hands-on Spark experience is a plus
- Practical understanding of distributed systems fundamentals: consensus, partitioning, replication, back-pressure, idempotency, and failure handling
- Experience with a cloud provider e.g. AWS, Azure, GCP (we use Google Cloud Platform)
- Experience with modern distributed and microservice technologies (we use Kubernetes, Kafka, and gRPC)
- Familiar with Software Development Lifecycle practices, such as continuous integration/continuous delivery and automated deployment (we use Docker, Kubernetes, and GitHub Actions)
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