StackAdapt is hiring a Machine Learning Engineer in Vancouver!
StackAdapt, an AI-driven programmatic advertising platform, is hiring a Machine Learning Engineer into its Data Science team. The group builds pipelines and ad-optimization models for a platform handling hundreds of billions of automated optimizations each second and databases serving millions of requests per second. The engineer works alongside Applied ML Scientists, other ML engineers, cross-functional engineering teams, and the CTO/co-founder. StackAdapt operates remote-first with distributed teams worldwide and will consider applicants based anywhere in Canada or the United States, so this is not a Vancouver-only role despite the posting location.
Key responsibilities
- Design modular, scalable real-time data pipelines capable of processing very large datasets
- Implement custom machine learning algorithms tuned for low-latency serving conditions
- Build microservice-based systems that handle training, inference, and monitoring across thousands of models running concurrently
- Partner with ML scientists and engineering colleagues to translate research approaches into production systems
- Turn loosely defined problems into concrete, actionable engineering plans
Key requirements
- Strong grounding in algorithm design, software design, concurrency, and data structures
- Hands-on experience implementing probabilistic or machine learning algorithms
- Interest in and aptitude for designing large-scale distributed systems
- Comfort operating in a low-latency, high-throughput environment
- Ability to break ambiguous problems into structured steps
- High academic standing from a respected computer science program
- Collaborative, friendly working style
- Seniority: Not specified
- Work mode: Remote-first (Canada or United States)
- Salary: $140,000 – $192,500 CAD base (Canada); $128,000 – $176,000 USD base (USA)
- Key skills: Real-time data pipelines, Machine learning algorithms, Distributed systems, Microservices, Concurrency, Data structures, Algorithm design, Low-latency inference, Model training and monitoring
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