Machine Learning Engineer

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- Design, build, and maintain scalable ML infrastructure to support notification relevance, targeting, ranking, and personalization across channels (e.g. push, email, in-app notifications) for millions of users. - Architect and implement end-to-end ML pipelines for notifications, including data ingestion, feature computation, model training, evaluation, deployment, and online serving in production environments. - Collaborate cross-functionally with data scientists, product managers, and engineers across teams to define requirements, align on priorities, and deliver end-to-end notification solutions. - Lead design and implementation of experimentation frameworks (e.g. A/B testing, multi-armed bandits, or similar methods) to evaluate new notification models, ranking strategies, and targeting policies. - Mentor and provide technical guidance to engineers, sharing best practices in machine learning engineering, distributed systems, and ML infrastructure design.

- Bachelor’s degree in Computer Science, Engineering (any field) or related quantitative discipline and five (5) years of experience in the job offered or related occupation. - (1) designing, building, and iterating large-scale scalable software systems; - (2) designing and building ML recommender systems at high scale; - (3) Delivering large and complex systems with significant business impact; - (4) Cross-functional collaboration on large-scale projects with complex dependencies across teams; - (5) Object-oriented programming (Python, Golang, or Java); - (6) Building ML models with PyTorch or TensorFlow; - (7) API design and integration with REST, HTTP, Thrift, or gRPC; - (8) Working with largescale key-value and NoSQL storage and caching systems (Redis, Cassandra, DynamoDB); - (9) Working with large-scale messaging and event-driven systems (Apache Kafka, AWS SQS, SNS, SES); - (10) Building workflows using orchestration systems (Kubeflow, Ray, Apache Airflow, AWS Step Functions); - (11) Working with large-scale analytics and data warehousing platforms (Google BigQuery, Amazon Redshift); - (12) Experience with observability, logging, tracing and monitoring tools such as Prometheus, Grafana, AWS Cloudwatch; - (13) Designing, running, and analyzing A/B experiments to measure and optimize system performance