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Salary: USD 350,000 - 430,000 / annual
Attentive is an AI marketing platform that combines SMS, RCS, email, and push notifications with an AI-powered personalization engine to deliver real-time, bespoke customer experiences. The company partners with over 8,000 customers across 70+ industries, including major brands like Crate and Barrel, Urban Outfitters, and Carter's, enabling billions of interactions that power tens of billions in revenue.
As a Principal Software Engineer in Machine Learning, you will lead the design, development, and operation of production-grade ML systems that power personalized experiences for hundreds of millions of customers. You will operate with high ownership, partner closely with Product and Engineering teams, and help raise the technical bar across ML systems in a fast-paced, high-impact environment.
Key responsibilities include:
- Building and scaling ML systems that maintain a high bar of quality and protect against regressions through comprehensive testing
- Collaborating across teams as a technical leader and communicator
- Continuously improving project quality through direct contributions and long-term advocacy for larger-scale changes
- Leading cross-functional machine learning projects
- Developing scalable, efficient, automated processes for large-scale data analyses, model development, validation, and implementation
The infrastructure stack includes Kubernetes on AWS EKS, with tooling like Istio, Datadog, Terraform, and Helm. The backend uses Java/Spring Boot microservices with DynamoDB, Kinesis, Airflow, Postgres, Planetscale, and Redis. ML work is driven by Python, Metaflow, HuggingFace, PyTorch, TensorFlow, and Pandas.
Attentive has a distributed global workforce with hubs in New York City, San Francisco, London, and Sydney. The company has been recognized by Deloitte's Fast 500 (four years running), LinkedIn's Top Startups, Forbes' Cloud 100 (five years running), Inc.'s Best Workplaces, and the Human Rights Campaign Foundation's Corporate Equality Index.
REQUIREMENTS:
- 10+ years of professional experience building systems
- Experience on a single system long enough to see the consequences of your decisions
- Proficiency in Python
- Experience with TensorFlow, PyTorch, xgboost, pandas, matplotlib, SQL, Spark, or similar tools
- Extensive experience using machine learning and data analysis to build scalable systems and data-driven products, working with cross-functional teams
- Proven track record of building scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation from modern research
- Experience leading cross-functional machine learning projects across teams
