Senior Machine Learning Systems Engineer

Reddit

Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$216,700–$303,400 / yr
Posted
today

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $214k
This role $260k
$154k most similar roles pay here $319k

This role pays more than 82% of similar roles. Most pay $177,253–$249,753 — the shaded band above. At the midpoint, this role pays about $260k versus about $214k for comparable roles.

Based on 240 similar postings.

Employer

About Reddit

Reddit is a social news aggregation and discussion platform where users share content, vote on posts, and engage in community conversations across thousands of interest-based forums called subreddits.

Reddit currently has 72 open roles on FindRole.

Listed pay typically runs $217,000–$303,900 across 66 roles with salary data.

Most-posted roles

View all roles at Reddit

At a glance

TL;DR · Senior Machine Learning Systems Engineer

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As a Senior ML Infrastructure Engineer on Reddit’s Machine Learning Platform team, you will lead the development of large-scale machine learning models and platforms that enhance user experience through improved content discovery and recommendation systems. Your daily tasks include designing end-to-end model lifecycle patterns for efficient MLOps, developing graph ML codebases to enable scalable model iteration, and optimizing batch data processing with tools like Apache Beam and Spark. You will also architect pipelines to manage massive graph data structures and collaborate closely with ML engineers on performance tuning in a distributed training environment. Ideal candidates have 5+ years of experience in ML infrastructure, hands-on expertise with cloud-based technologies such as GCP BigQuery and Terraform, proficiency in Python, PyTorch, TensorFlow, and familiarity with MLOps tools like MLflow or Wandb. Experience with graph neural networks and frameworks like PyTorch Geometric is a significant plus.

Skills

Python PyTorch Tensorflow Kubernetes Ray Apache Beam Apache Spark Ray Data GCP BigQuery Google Cloud Storage Terraform MLflow Wandb Neo4j JanusGraph TigerGraph PyTorch Geometric Deep Graph Library

What you'll do

  • Design end-to-end model lifecycle patterns to boost ML development velocity.
  • Develop and support a graph ML codebase for scalable and efficient model iteration.
  • Collaborate on performance tuning of models in a large, distributed training environment.
  • Optimize batch data processing within a data warehouse using Apache Beam and Spark.
  • Architect pipelines to build and maintain massive graph data structures with billions of nodes.

What we're looking for

  • 5+ years experience in ML infrastructure and model deployments.
  • Expertise in cloud-based technologies for ML platforms like GCP BigQuery, Google Cloud Storage.
  • Hands-on experience with MLOps tools such as MLflow or Wandb.
  • Proficiency in Python, PyTorch, TensorFlow, and distributed training frameworks (Ray, Kubernetes).
  • Deep understanding of graph databases and neural networks (Neo4j, PyTorch Geometric).

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