Machine Learning Engineer

Job Duties: 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.Requirements: 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.Special Skill Requirements: (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); (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.