About the Team
The LS Embedding team focuses on developing highly expressive, multi-entity embeddings to enhance Reddit’s recommendation systems. We go beyond standard two-tower architectures, leveraging graph-based modeling, Graph Neural Networks (GNNs), and transformer-based architectures to capture complex interactions between users and entities. Our work directly impacts personalization and relevance across Reddit’s platform.
About the Role
We are seeking a Senior Machine Learning Engineer to design, develop, and optimize graph-based ML models for large-scale recommendation systems. You will work on embedding generation, distributed training, and scalable serving architectures, playing a key role in improving Reddit’s AI-powered personalization. This role offers the opportunity to contribute to cutting-edge ML research and apply it at scale in a high-impact production environment.
Responsibilities
- Design and implement scalable, high-performance machine learning models using Graph Neural Networks (GNNs), transformers, and knowledge graph approaches.
- Develop and optimize large-scale embedding generation pipelines for Reddit’s recommendation systems.
- Collaborate with ML infrastructure teams to enable efficient distributed training (multi-GPU, model/data parallelism) and low-latency serving.
- Work closely with cross-functional teams (Ads, Feed Ranking, Content Understanding) to integrate embeddings into various personalization and ranking systems.
- Drive feature engineering efforts, identifying and curating expressive raw data to enhance model effectiveness.
- Monitor, evaluate, and improve model performance using A/B testing, offline metrics, and real-time feedback loops.
- Stay up-to-date with the latest research in GNNs, transformers, and representation learning, bringing new ideas into production.
- Participate in code reviews, mentor junior engineers, and contribute to technical decision-making.
Qualifications
- 5+ years of experience in machine learning engineering, with a strong focus on recommendation systems, representation learning, and deep learning.
- Hands-on experience with Graph Neural Networks (GNNs), collaborative filtering, and large-scale embeddings.
- Proficiency in Python and experience with ML frameworks such as PyTorch Geometric (PyG), Deep Graph Library (DGL), TensorFlow, or JAX.
- Strong understanding of graph theory, network science, and representation learning techniques.
- Experience building distributed training and inference systems using ML infrastructure components (data parallelism, model pruning, inference optimization, etc.).
- Ability to work in a fast-paced environment, balancing innovation with high-quality production deployment.
- Strong communication skills and the ability to collaborate cross-functionally with engineers, researchers, and product teams.
Benefits:
- Comprehensive Healthcare Benefits and Income Replacement Programs
- 401k Match
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Flexible Vacation & Reddit Global Days off
- Generous paid Parental Leave
- Paid Volunteer time off
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 pay 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.
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