Senior Software Engineer, Ml/ai Platform

Attentive Attentive · Enterprise · United States · Engineering

Senior Software Engineer to join the ML Platform team, responsible for building and operating the ML data, tooling, serving, and inference layers of the ML platform. This role will enable Attentive's ML practice to directly impact the AI product suite through tools to train, serve, and deploy ML models with higher velocity and performance, while maintaining reliability.

What you'd actually do

  1. Unlock offline & real-time access to trillions of data points for our ML and Data Science teams.
  2. Manage, expand, and optimize our feature store that enables feature engineering, multi-TB scale training jobs, and offline / real-time inferencing.
  3. Support PB scale data operations on the feature store using Apache Spark, Spark Structured Streaming, Kafka, and Ray.
  4. Partner with other teams and business stakeholders to deliver ML and AI initiatives.

Skills

Required

  • Data Engineering / MLOps for 5+ years
  • built and matured PB-scale feature store pipelines
  • deep Apache Spark, Spark Streaming, and Ray Data experience
  • built data pipelines for ML use cases
  • understand data cardinality, query plans, configuration settings, and hardware impact on data pipeline performance
  • infrastructure for Training ML models/fine-tuning LLMs
  • understand key differences between online and offline ML inferences

Nice to have

  • Kubernetes
  • AWS EKS
  • Istio
  • Datadog
  • Terraform
  • CloudFlare
  • Helm
  • Java / Spring Boot
  • DynamoDB
  • Kinesis
  • AirFlow
  • Postgres
  • Planetscale
  • Redis
  • React
  • TypeScript
  • GraphQL
  • Storybook
  • Radix UI
  • Vite
  • esbuild
  • Playwright
  • Python
  • Metaflow
  • HuggingFace
  • PyTorch
  • TensorFlow
  • Pandas

What the JD emphasized

  • built and matured the pipelines of a PB-scale feature store
  • deep Apache Spark, Spark Streaming, and Ray Data experience
  • built data pipelines for ML use cases using these tools
  • infrastructure for Training ML models/fine-tuning LLMs
  • key differences between online and offline ML inferences

Other signals

  • ML Platform
  • MLOps
  • Feature Store
  • Training Data
  • Inference