Staff Machine Learning Engineer - LS Embedding
Job Summary
The role involves leading the development of advanced multi-entity embedding models utilizing Graph Neural Networks, transformers, and representation learning techniques to improve personalization and recommendations on Reddit. It requires extensive experience in scalable machine learning architectures, graph-based learning, and deployment in large-scale systems. The candidate will collaborate cross-functionally, mentor engineers, and drive research initiatives to keep Reddit at the forefront of AI innovations. The position offers opportunities to shape AI-driven systems, influence strategic ML directions, and work with cutting-edge technologies.
Required Skills
Benefits
Job Description
About the Team
The LS Embedding Team is at the forefront of building highly expressive, multi-entity embeddings that power Reddit’s recommendation systems. We go beyond standard two-tower architectures, leveraging Graph Neural Networks (GNNs), large-scale transformer models, and cutting-edge representation learning techniques to enhance personalization across Reddit’s ecosystem. Our work impacts content discovery, user engagement, and platform growth at a massive scale.
About the Role
As a Staff Machine Learning Engineer, you will own the technical direction for large-scale embedding models, guiding the development of state-of-the-art graph-based ML architectures and high-impact representation learning strategies. You will partner with leadership to define ML roadmaps, drive innovation in large-scale graph embeddings and deep learning techniques, and ensure scalable, efficient deployment of ML models in production. This role offers an opportunity to influence key AI-driven systems across Reddit while mentoring and uplifting the team’s technical capabilities.
Responsibilities
- Architect and lead the development of next-generation multi-entity embeddings, leveraging Graph Neural Networks (GNNs), transformers, and large-scale representation learning techniques.
- Define and execute the ML strategy for embedding models, identifying opportunities to enhance personalization and recommendation quality across Reddit.
- Lead research initiatives on scalable graph-based learning, self-supervised techniques, and real-time adaptation, bringing cutting-edge advancements into production.
- Partner with ML infrastructure teams to build high-performance, distributed training systems that efficiently scale across multiple GPUs and cloud environments.
- Establish and optimize real-time serving architectures for large-scale embeddings, ensuring low-latency inference and high throughput.
- Collaborate cross-functionally with teams in Feed Ranking, Ads, Content Understanding, and Core ML to integrate embeddings into Reddit’s key AI-driven systems.
- Mentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharing.
- Stay at the forefront of AI research, evaluating and introducing new modeling paradigms to keep Reddit’s ML ecosystem at the cutting edge.
- Drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-making.
Qualifications
- 8+ years of experience in machine learning engineering, with a strong focus on representation learning, large-scale embeddings, and recommendation systems.
- Expertise in Graph Neural Networks (GNNs), graph-based representation learning, and transformer architectures.
- Deep understanding of graph theory, knowledge graphs, and complex multi-entity relationships in machine learning applications.
- Proven ability to design, implement, and optimize scalable ML architectures, from distributed training to real-time inference.
- Hands-on experience with PyTorch Geometric (PyG), Deep Graph Library (DGL), TensorFlow, JAX, and large-scale ML model optimization.
- Strong software engineering skills in Python, C++, or similar languages, with experience in ML infrastructure, high-performance computing, and cloud-based ML pipelines.
- Demonstrated leadership in driving ML strategy, mentoring engineers, and influencing cross-functional teams.
- Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in large-scale production systems.
- Excellent communication skills, with the ability to effectively present complex ML concepts to technical and non-technical stakeholders.
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.
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.
A social media platform where users create and participate in communities (subreddits) to discuss a wide range of topics, share content, and connect.
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