Machine Learning Engineer

Why Attentive needs you

• You have a proven track record of building systems that maintain a high bar of quality
• You deeply loathe regressions and take proactive steps to protect against them through a variety of testing techniques
• You are a collaborator, technical leader, and a great communicator
• You are constantly improving the quality of the project you are working on, both via direct contributions as well as long-term advocacy for larger-scale changes
• You are enthusiastic about the high impact, fast-paced work environment of an late-stage startup

About you

• You have worked professionally building systems for 3+ years with experience on a single system long enough to see the consequences of your decisions
• Experience with TensorFlow/Pytorch, xgboost, pandas, matplotlib, SQL, Spark or similar tools
• You have proficiency or experience with Python
• You have extensive experience using machine learning and data analysis, or similar, to build scalable systems and data-driven products, working with cross-functional teams
• You have a proven track record of building scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation from modern research

Our scale

• 8,000 brands powered by Attentive sent over 2.2 billion text messages over Cyber Week 2023 (Black Friday/Cyber Monday) representing a growth of 31% from 2022
• We sent 32 billion SMS messages in 2023, up 32% YoY. That’s an average of 87 million per day
• Our production cluster contains over 18,000 containers which serve 200+ services
• Our streaming services process over 80 billion events per month

What we use

• Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKSInfrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
• Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS
• Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
• Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas