Staff Machine Learning Systems Engineer
Remote - United States
- Pay
USD 230,000–322,000/year · BaseAnnual period assumed — pay source
The base salary range for this position is: $230,000—$322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.
Read the full posting- Work setup
Remote stated — work setup source
Listed location: Remote - United States
Read the full posting- Employment
- Unconfirmed
What you’ll work on
Full postingDesign end-to-end MLOps patterns covering data prep, model management, and experiment tracking
Build and support a graph ML codebase and platform from zero to one
Tune model training performance and GPU costs in a distributed training environment
Optimize batch data processing with tools such as Apache Beam, Apache Spark, and Ray Data
Architect pipelines for graph data structures with billions of nodes and tens of billions of edges
From the employer’s posting
Design end-to-end model lifecycle patterns (MLOps) to boost velocity of development for ML engineers, including data preparation, model management, experiment tracking, and more
Zero-to-one development and support of a graph ML codebase and platform that abstracts away common patterns and enables greater model scalability and iteration
Collaborate with ML engineers on performance tuning, including improving model training time, efficiency, and GPU training costs in a large, distributed ML training environment
Optimize batch data processing within a data warehouse and with tools such as Apache Beam, Apache Spark, Ray Data, and more
Architect pipelines to build and maintain massive graph data structures on the order of billions of nodes and tens of billions of edges
Tools in this posting
- Python
- BigQuery
- Google Cloud (GCP)
- Kubernetes
- MLflow
- Neo4j
- Spark
- Terraform
- PyTorch
- TensorFlow
- Google Cloud Storage
Source — Tool mentions in context
- Hands-on experience administering and integrating MLOps tools for experiment tracking, model serving, and model registries (e.g. MLflow or Wandb) - Proficiency with the common programming languages and frameworks of ML, such as Python, PyTorch, Tensorflow, etc. - Deep experience working with distributed training frameworks, including Ray and Kubernetes
- Hands-on experience with ML optimization, including memory and GPU profiling - Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and more - Hands-on experience administering and integrating MLOps tools for experiment tracking, model serving, and model registries (e.g. MLflow or Wandb)
- Proficiency with the common programming languages and frameworks of ML, such as Python, PyTorch, Tensorflow, etc. - Deep experience working with distributed training frameworks, including Ray and Kubernetes - Strong focus on scalability, reliability, performance, and ease of use. You are an undying advocate for platform users and have a deep intuition for the machine learning development lifecycle.
- Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and more - Hands-on experience administering and integrating MLOps tools for experiment tracking, model serving, and model registries (e.g. MLflow or Wandb) - Proficiency with the common programming languages and frameworks of ML, such as Python, PyTorch, Tensorflow, etc.
- Strong organizational & communication skills - Experience working with graph databases (Neo4j, JanusGraph, TigerGraph) is a big plus - Experience working with graph neural networks (GNNs) and associated graph ML frameworks (PyTorch Geometric, Deep Graph Library) is a big plus
- Collaborate with ML engineers on performance tuning, including improving model training time, efficiency, and GPU training costs in a large, distributed ML training environment - Optimize batch data processing within a data warehouse and with tools such as Apache Beam, Apache Spark, Ray Data, and more - Architect pipelines to build and maintain massive graph data structures on the order of billions of nodes and tens of billions of edges
- Experience working with graph databases (Neo4j, JanusGraph, TigerGraph) is a big plus - Experience working with graph neural networks (GNNs) and associated graph ML frameworks (PyTorch Geometric, Deep Graph Library) is a big plus Pay Transparency:
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Apr 14, 2026 · 187d ago
- Recorded sightings
- 336
- Last seen by us
- Oct 17, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Job description
Who We Are:
The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure that powers recommendations, content discovery, user and content quantification, while directly impacting other teams such as Growth, Ads, Feeds, and Core Machine Learning teams.
What You’ll Do:
As a Staff ML Infrastructure Engineer, you will lead development of a platform for large scale ML models at Reddit.
- Design end-to-end model lifecycle patterns (MLOps) to boost velocity of development for ML engineers, including data preparation, model management, experiment tracking, and more
- Zero-to-one development and support of a graph ML codebase and platform that abstracts away common patterns and enables greater model scalability and iteration
- Collaborate with ML engineers on performance tuning, including improving model training time, efficiency, and GPU training costs in a large, distributed ML training environment
- Optimize batch data processing within a data warehouse and with tools such as Apache Beam, Apache Spark, Ray Data, and more
- Architect pipelines to build and maintain massive graph data structures on the order of billions of nodes and tens of billions of edges
Who You Might Be:
- 8+ years of experience in ML infrastructure, including model training and model deployments
- Hands-on experience with ML optimization, including memory and GPU profiling
- Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and more
- Hands-on experience administering and integrating MLOps tools for experiment tracking, model serving, and model registries (e.g. MLflow or Wandb)
- Proficiency with the common programming languages and frameworks of ML, such as Python, PyTorch, Tensorflow, etc.
- Deep experience working with distributed training frameworks, including Ray and Kubernetes
- Strong focus on scalability, reliability, performance, and ease of use. You are an undying advocate for platform users and have a deep intuition for the machine learning development lifecycle.
- Strong organizational & communication skills
- Experience working with graph databases (Neo4j, JanusGraph, TigerGraph) is a big plus
- Experience working with graph neural networks (GNNs) and associated graph ML frameworks (PyTorch Geometric, Deep Graph Library) is a big plus
Pay Transparency:
This job posting may span more than one career level.
In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.
To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.
In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.
During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.
Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.
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- Pay
The base salary range for this position is: $230,000—$322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.
- Location & working pattern
Remote - United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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