below marketmost similar roles pay hereabove market
This role pays more than
99%
of similar roles. Most pay
$207,300–$254,750
— the blue band above.
At the midpoint, this role pays about
$351k
versus about
$231k
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
89 open roles
on FindRole.
Listed pay typically runs
$216,700–$303,000
across 89 roles with salary data.
The Senior Staff Machine Learning Systems Engineer joins the Ads ML Platform team to lead the technical strategy for the end-to-end Ads ML engineer lifecycle. This role focuses on accelerating the development of features, training data, and offline experimentation workflows to improve model iteration speed. You will define architectures for batch and streaming computation, lineage, quality, and observability while building platform abstractions and workflow automation. The position requires expertise in distributed data and compute systems including Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, and Databricks. You will solve technical friction points by creating reusable platform capabilities that bridge the gap between offline model development and production impact, ultimately ensuring that production ML systems are built, scaled, and operated reliably across the Ads product area.
Own the technical strategy for the end-to-end Ads ML engineer lifecycle.
Define architecture and technical standards for ML feature and training-data systems.
Build platform abstractions and workflow automation to improve ML development velocity.
Align Ads ML platform priorities with the broader Reddit ML Platform vision.
Extend platform strategy into serving and online experimentation workflows.
Partner across engineering teams to clarify ownership and drive durable execution.
Mentor Staff and senior engineers to raise the architecture and operational bar.
What we're looking for
You have 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems.
You have 4+ years of experience building or operating production ML infrastructure, feature platforms, training data systems, or experimentation systems.
You have led broad, ambiguous, multi-team platform initiatives from strategy through adoption.
You have built platforms used directly by ML engineers, data scientists, or product teams developing production ML systems.
You have deep experience in ML platform, feature platform, training data, experimentation, developer infrastructure, or distributed data infrastructure.
You have worked with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, or Databricks.
You can influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, and decision frameworks.
You have experience mentoring Staff and senior engineers to raise architecture and operational bars.
Python
Java
C++
PyTorch
TensorFlow
XGBoost
Large Language Models
Deep Learning
Natural Language Processing
Retrieval-Augmented Generation
RAG
Sequence Modeling
Information Retrieval
Recommendation Systems
A/B Testing
Feature Engineering
Prompt Engineering