Staff Machine Learning Engineer, Ai Serving

Reddit, Inc.

Remote, US
Base: $253,300 - $354,600 usd; equity: eligible fo...
Remote
7+ years ml engineering experience
Kubernetes orchestration at scale
Python and go programming proficiency
Reddit is seeking a Staff Machine Learning Engineer for its remote team, focusing on developing a robust ML Inference Platform. The ideal candidate should have extensive experience in ML engineering, cloud technologies, and proficiency in programming languages, particularly Python

Job Summary

  • The role involves leading the end-to-end design and maintenance of a highly available, low-latency GPU-based model serving system supporting millions of QPS.
  • Candidates will develop high-performance feature hydration and processing systems including routing, caching, and batching within cloud-based Kubernetes environments.
  • Reddit offers comprehensive benefits including healthcare, 401k matching, gender-affirming care, and generous paid parental leave.

Matching Summary

Match Score: 85

Reddit is seeking a Staff Machine Learning Engineer for its remote team, focusing on developing a robust ML Inference Platform. The ideal candidate should have extensive experience in ML engineering, cloud technologies, and proficiency in programming languages, particularly Python.

Salary

Base: $253,300 - $354,600 USD; Equity: Eligible for restricted stock units; Benefits: Comprehensive healthcare, 401k match, paid time off

Skills & Requirements

Must-have

  • 7+ years ML Engineering experience
  • Kubernetes orchestration at scale
  • Python and Go programming proficiency
  • AWS or Google Cloud infrastructure
  • GPU model serving and optimization

Nice-to-have

  • Experience with LLM serving online
  • Knowledge of Triton, Dynamo, vLLM
  • Strong communication with stakeholders
  • E2E inference benchmarking framework
  • Deep intuition for genAI lifecycle

Key Requirements

  • 7+ years in ML Engineering or AI Platform roles
  • Proficiency in Python and Go languages
  • Experience operating Kubernetes at scale
  • Cloud-based technology expertise (AWS/GCP)
  • Deep understanding of multi-cluster compute environments

Work Rights

Not specified

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