Senior Data Engineer, Machine Learning Operations - Attentive

Job Title: Senior Data Engineer, Machine Learning Operations
Job Location: San Francisco, CA
Job Listing URL: https://jobs.lever.co/attentive/2ac86fed-5921-4beb-91d1-36a0928ad861
Job Description: Attentive® is the AI-powered mobile marketing platform transforming the way brands personalize consumer engagement. Attentive enables marketers to craft tailored journeys for every subscriber, driving higher recurring revenue and maximizing campaign performance. Activating real-time data from multiple channels and advanced AI, the platform personalizes content, tone, and timing to deliver 1:1 messages that truly resonate.

With a top-rated customer success team recognized on G2, Attentive partners with marketers to provide strategic guidance and optimize SMS and email campaigns. Trusted by leading global brands like Neiman Marcus, Samsung, Wayfair, and Dyson, Attentive ensures enterprise-grade compliance and deliverability, supporting trillions of interactions across more than 70 industries. To learn more or request a demo, visit www.attentive.com or follow us on LinkedIn, X (formerly Twitter), or Instagram.

Attentive’s growth has been recognized by Deloitte’s Fast 500, Linkedin’s Top Startups and Forbes Cloud 100 all thanks to the hard work from our global employees!

Who we are
We’re looking for a self-motivated, highly driven Senior Software Engineer to join our Machine Learning Operations (MLOps) team. As a team, we enable Attentive’s Machine Learning (ML) practice to directly impact Attentive’s AI product suite through the tools to train, inference, and deploy ML models with higher velocity and performance, while maintaining reliability. We build and maintain a foundational ML platform spanning the full ML lifecycle for consumption by ML engineers and data scientists. This is an exciting opportunity to join a rapidly growing MLOps team at the ground floor with the ability to drive and influence the architectural roadmap enabling the entire ML organization at Attentive.

This team and role is responsible for building and operating the ML data, tooling, serving, and inference layers of the ML platform. We are excited to bring on more engineers to continue expanding this stack.
Why Attentive needs you
Unlock offline & real-time access to trillions of data points for our ML and Data Science teams
Manage, expand, and optimize our feature store that enables feature engineering, multi-TB scale training jobs, and offline / real-time inferencing
Support PB scale data operations on the feature store using Apache Spark, Spark Structured Streaming, Kafka, and Ray
Partner with other teams and business stakeholders to deliver ML and AI initiatives

About you
You have been working in the areas of Data Engineering / MLOps for 5+ years, and have built and matured the pipelines of a PB-scale feature store
You have deep Apache Spark, Spark Streaming, and Ray Data experience and built data pipelines for ML use cases using these tools
You understand the correlation between data cardinality, query plans, configuration settings, and hardware and the impact of each on data pipeline performance
You have led the rollout and operationalization of feature stores such as Tecton or Feast
You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs
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