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AI Stream Processing ML Engineer

Grayson Martins

Full-time · Job · Chicago

About the role

Batch ML is yesterday's problem. Our clients need predictions that update as data arrives — fraud scores that reflect the transaction happening right now, recommendation rankings that incorporate the click from 3 seconds ago, demand forecasts that adjust when a supplier announces a delay. We build the streaming ML infrastructure that makes this possible. The technical challenge is maintaining model accuracy when features arrive out of order, data sources have different latencies, and the feature store needs to serve both real-time and historical values for the same entity. You'll design the feature computation framework that handles these temporal complexities while keeping feature definitions consistent between training and serving — the infamous train-serve skew problem. The streaming team is five engineers. You'll be the senior ML engineer who bridges the gap between data infrastructure and model development, ensuring that the real-time features we compute actually improve model performance versus their batch equivalents.

Responsibilities

  • Design streaming feature computation frameworks for real-time ML
  • Solve train-serve skew by ensuring feature consistency across environments
  • Build temporal handling for out-of-order and multi-latency data sources
  • Evaluate the impact of real-time features versus batch equivalents on model performance
  • Architect the integration between streaming infrastructure and feature stores
  • Mentor junior engineers on streaming ML best practices

Requirements

  • 5+ years in data or ML engineering with streaming systems experience
  • Deep expertise with Kafka, Spark Streaming, or Flink
  • Understanding of feature store architectures and train-serve consistency
  • Strong Python and distributed systems skills
  • Experience debugging temporal data issues in production ML systems

Benefits

  • Fully remote with flexible schedule
  • Competitive salary plus annual bonus
  • Health, dental, and vision insurance
  • 401(k) with employer match
  • $4,000 annual conference and learning budget

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Categories

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python spark kafka docker aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python spark kafka docker aws pytorch

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