Staff Machine Learning Engineer, Consumer at Reddit

Confirmed open

Reddit is hiring a staff machine learning engineer, consumer focused on consumer machine learning systems.

Reddit

Location: Remote
Work arrangement: remote
Technologies: Python, PyTorch, TensorFlow, Go

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Role overview

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 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .

At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning.

We are hiring Machine Learning Engineers across our Consumer Engineering organization, giving you the opportunity to work on a wide range of high-impact problems across the Consumer ecosystem. We are looking for Machine Learning Engineers who are excited to build systems end-to-end, from research and modeling to production deployment, and who want to help shape the future of discovery, relevance, and monetization at Reddit.

If you love working on complex, real-world ML problems at massive scale, this role is for you.

We are looking for a Staff Machine Learning Engineer to help drive the next generation of Reddit’s ML ecosystem across recommendations, search, messaging, and foundational AI systems. You will lead high-impact initiatives from ideation to production, shaping both technical strategy and product direction across multiple ML domains. This is a highly cross-functional role partnering with Product, Data Science, and Engineering to deliver meaningful user and business impact.

This role sits at the intersection of:

Relevance & recommendation systems (content, search, notifications)

AI-powered discovery & LLM-driven experiences

Content and user understanding & large-scale representation learning

Large-scale ML infrastructure and pipelines

Responsibilities

Set technical direction and deliver systems for consumer machine learning systems.
Collaborate with product, data, security and engineering teams.
Improve reliability, performance, testing, automation and documentation.
Mentor engineers and guide sustainable production practices.

Qualifications

7+ years of experience building, deploying, and operating machine learning systems in production
Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc)
Expert at developing and productionizing models using TensorFlow, PyTorch, or Hugging Face Transformers
Experience building production-quality code incorporating testing, evaluation, and monitoring using object-oriented programming, including experience in Python and Golang
Experience designing and scaling ML systems, including data pipelines, feature engineering, model training/serving, and production monitoring
Excellent communication and collaboration skills, with the ability to discuss complex technical topics with diverse teams and translating product needs into scalable ML solutions
Track record of driving measurable impact through applied machine learning in real-world products
Subject matter expertise in one of the following domains:
Search systems (lexical and semantic retrieval and ranking)
Content understanding (NLU/NLP/LLM, topic/taxonomy modeling, interest graphs or clustering, and multimodal understanding)
Familiarity with distributed systems and large-scale data processing frameworks (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)
Experience working with real-time systems and low-latency production environments
Experience with LLM/GenAI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale
Strong experimentation rigor, with experience formulating clear hypotheses, designing actionable learning plans and building offline/online correlations
Advanced degree in Computer Science, Machine Learning, or related quantitative field

Technology in this role

Python: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .
PyTorch: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .
TensorFlow: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .
Go: Listed by the employer as part of the role’s technical requirements.
Golang: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .
Redis: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .
Kafka: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .
Spark: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .
Airflow: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .
LLM: This role sits at the intersection of: Relevance & recommendation systems (content, search, notifications) AI-powered discovery & LLM-driven experiences Content and user understanding & large-scale representation learning Large-scale ML infrastructure and .
RAG: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .
BigQuery: 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models .

Benefits

Comprehensive Healthcare Benefits and Income Replacement Programs
Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
Mental Health & Coaching Benefits
Flexible Vacation & Paid Volunteer Time Off
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.
The base salary range for this position is:
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