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 6+ 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 EKS
- Infrastructure 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
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