This role pays more than
99%
of similar roles. Most pay
$202,625–$260,050
— the shaded 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
77 open roles
on FindRole.
Listed pay typically runs
$217,000–$303,400
across 77 roles with salary data.
As a Senior Staff Machine Learning Systems Engineer on the Ads ML Platform team, you will lead the technical strategy for the end-to-end Ads ML engineer lifecycle. You will focus on accelerating the feature development and training iteration loop by building platform abstractions and workflow automation to make machine learning development faster and more reliable. Your role involves defining architecture and standards for feature systems across batch/streaming computation, backfills, lineage, and quality. You will solve complex problems regarding how production ML systems are built, scaled, and operated within the advertising domain. To succeed, you must possess expertise in distributed data and compute systems including Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, or Databricks. You will also leverage your experience in infrastructure, training data systems, and experimentation platforms to drive durable technical solutions.
Lead the technical strategy for the end-to-end Ads ML engineer lifecycle including feature development and training data.
Define architecture and technical standards for ML feature systems across batch/streaming computation, lineage, and observability.
Build platform abstractions and workflow automation to make ML development faster, safer, and more self-service.
Identify and resolve high-leverage friction points in the daily development workflows of ML engineers.
Expand the platform strategy into serving and online experimentation workflows for seamless offline-to-online transitions.
Align Ads ML platform priorities with broader company goals to create reusable capabilities across different teams.
Mentor staff and senior engineers while raising the architectural and operational standards for the engineering team.
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 building or operating production ML infrastructure, feature platforms, training data systems, experimentation systems, or large-scale data pipelines.
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, Databricks, or similar technologies.
You can influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms.
Large Language Models
Python
PyTorch
TensorFlow
XGBoost
Natural Language Processing
Information Retrieval
Ranking and Relevance
Recommendation Systems
RAG
Prompt Engineering
C++
Java
A/B Testing
Feature Engineering
Deep Learning
Sequence Modeling