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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 machine learning-powered personalization to help brands build authentic customer relationships. The company serves 8,000+ customers across 70+ industries and has been recognized by Deloitte (Fast 500), Forbes (Cloud 100), and LinkedIn as a top startup.
The Machine Learning Engineering team powers personalized experiences for hundreds of millions of customers across thousands of brands. As a Principal Software Engineer in Machine Learning, you will play a critical role in building, scaling, and operating production-grade ML systems that drive real-time personalization across the Attentive platform. 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 systems that maintain a high bar of quality and proactively protect against regressions through comprehensive testing techniques
- Collaborating as a technical leader and strong communicator across cross-functional teams
- Continuously improving project quality through direct contributions and long-term advocacy for larger-scale changes
- Leading cross-functional machine learning projects
- Building scalable, efficient, automated processes for large-scale data analyses, model development, validation, and implementation
The tech stack includes Kubernetes/AWS EKS, Istio, Datadog, Terraform, Java/Spring Boot microservices, DynamoDB, Kinesis, AirFlow, Postgres, Redis, Python, Metaflow, HuggingFace, PyTorch, TensorFlow, and Pandas.
Requirements:
- 10+ years of professional experience building systems, with 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
- Proven track record of building scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation
- Experience leading cross-functional machine learning projects across teams
