Machine Learning Engineer, Ads Training Platform New at Reddit
Remote, Oregon, USA -
Full Time


Start Date

Immediate

Expiry Date

16 Nov, 25

Salary

260100.0

Posted On

16 Aug, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

Reddit is a community of communities. It’s built on shared interests, passion, and trust and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 101M+ daily active unique visitors, Reddit is one of the internet’s largest sources of information.
Reddit has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you’d like. Don’t live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence.
The Ads Training Platform pod builds and maintains the distributed training and data processing infrastructure that powers Reddit’s Ads machine learning models. We focus on enabling fast, reliable, and scalable model training across large datasets, supporting the Ads ML teams in improving ad targeting, conversion prediction, and advertiser value.
We are looking for an engineer with deep experience in infrastructure, distributed systems, and ML platform operations to help evolve and scale our training systems.

How To Apply:

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Responsibilities

Design, build, and maintain large-scale distributed training infrastructure for Ads ML models.

  • Develop tools and frameworks on top of the Ray platform.
  • Build tools to debug, profile, and tune distributed training jobs for performance and reliability.
  • Integrate with object storage systems and improve data access patterns.
  • Collaborate with ML engineers to improve model training time, efficiency, and GPU training costs..

    • Drive improvements in scheduling, state management, and fault tolerance within the training platform to enhance overall performance.
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