Senior Backend Developer, AI Systems
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Movable InkMarketing personalization
Location: Movable Ink - TorontoFull-TimeSenior
SalaryThe base pay range for this position is $143k-$200k CAD/year, which can include additional bonus depending on the position ultimately offered
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Job Details
- Experience
- 5+ years software engineering experience with a focus on distributed systems
- Required Skills
- PythonGCPGitSparkCI/CDUnit TestingDistributed Systems
Requirements
- Have 5+ years of software engineering experience focused on distributed systems.
- Bring deep experience building and operating large-scale distributed systems that are fault tolerant, highly available, and highly concurrent.
- Demonstrate software engineering skills in system design, unit testing, Git, code review, and CI/CD.
- Have a strong foundation in Python.
- Have experience with distributed data processing frameworks such as Apache Spark, Apache Flink, or Apache Beam; hands-on Spark experience is a plus.
- Understand distributed systems fundamentals including consensus, partitioning, replication, back-pressure, idempotency, and failure handling.
- Have experience with a cloud provider such as AWS, Azure, or GCP.
- Have experience with modern distributed and microservice technologies; the team uses Kubernetes, Kafka, and gRPC.
- Be familiar with software development lifecycle practices, continuous integration and delivery, and automated deployment; the team uses Docker, Kubernetes, and GitHub Actions.
- Bring technical leadership skills to collaborate, advocate for architectural decisions, and mentor engineers.
- Be able to identify, analyze, and resolve complex production-system issues.
Responsibilities
- Lead the architectural evolution of the core recommendations pipeline to a modern distributed architecture while maintaining production stability.
- Design, build, and operate scalable, fault-tolerant backend services for content selection, send-time optimization, subject line personalization, and frequency capping.
- Identify and resolve performance bottlenecks, reliability risks, and scaling limitations.
- Collaborate with ML engineers and product stakeholders to translate requirements into technical designs with a path to production.
- Own system components from architecture and implementation through deployment, monitoring, and incident response.
- Champion engineering best practices in system design, code quality, testing, observability, and operational excellence.
- Mentor engineers and contribute to technical rigor, ownership, and continuous improvement.
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