We’re looking for a Senior Staff Machine Learning Engineer to join our Feed Relevance team, which is responsible for the end-to-end systems that power personalization and ranking for the main Reddit feeds. This is a critical role that blends hands-on development with technical leadership, focusing on building scalable, extensible, and highly performant personalization systems. You will be instrumental in defining the technical direction for the team, collaborating closely with Product and Engineering leadership to set the strategy, and working with other individual contributors to execute and scale our systems to serve tens of millions of users. This work will directly enable our Machine Learning Engineers (MLEs) and engineers to quickly iterate and continuously improve the user experience. You will also partner with Infra and Platform teams to inform the direction of our core infrastructure, building for the future of ML at Reddit.

Responsibilities:

• Deliver on technical initiatives that have significant company-wide impact
• Set technical direction for the broader Relevance and Feeds teams at Reddit, able to identify opportunities and influence strategy across multiple orgs
• Work with management on goal setting, planning, and de-risking critical projects
• Mentor and grow Senior and Staff engineers
• Create a strong healthy engineering culture

Qualifications:

• 10+ years of industry experience building systems for relevance driven products
• Subject matter expert in relevance, recommendation, and ML systems; able to solve complex problems in these domains that few others can 
• Deep understanding of how to build sustainable software systems at a large scale engineering organization
• Experience in influencing organizations on technical direction/best practices
• Experience working with cross-functional teams such as design, product, business & data teams to deliver great experiences.
• Strong focus on user experience, usability, scalability, reliability and quality. You are an undying advocate for the user, and you have a deep intuition for how people & machines interact with software at scale.
• High empathy, excellent communication skills, and the ability to find compromise working across the entire engineering org

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

#LI-remote, #LI-JS5

The Developer Platform team is building the future of Reddit: a future where third-party developers, moderators and everyday redditors can invent and ship brand-new apps, games and tools that create entirely new ways for communities to play, connect and get things done at reddit. We power everything from interactive custom posts and daily games to mod tooling and data products, giving builds the primitives they need to create entirely new kinds of  experiences on Reddit. 

Within Developer Platform, our Growth team focuses on helping redditors discover the right apps and games at the right time through ranking, notifications, and cross‑surface entry points, directly moving the needle on engagement and DAU for Dev Platform experiences. You’ll work end‑to‑end with a highly collaborative, cross‑functional group of engineers, PMs, designers, and data scientists who value impact, personal growth, openness, and kindness, and who ship quickly while holding a high bar for reliability and performance across Reddit’s web, mobile, and backend stack.

How You'll Have Impact

We are looking for a Staff Machine Learning Engineer to build the ranking and personalization systems that connect redditors with their next favorite game or app on Reddit. You will work closely with Product, Backend, Data Science, and Core Ranking/ML Platform teams to design and ship best-in-class ranking, retrieval, and experimentation systems that power discovery and re-engagement for Dev Platform experiences across feeds, surfaces, and notifications.

You will own problems end-to-end—from framing objectives and defining signals, to training and deploying models, to designing experiments and reading results—not just tuning existing knobs on mature systems. You’ll help define the ranking strategy for Developer Platform, stand up new ML models and feedback loops where none exist today, and turn noisy behavioral data into clear wins for users, creators, and developers.

This is a high-impact, 0→1 role where your work will directly shape how interactive apps and games show up on one of the largest sites in the world. You’ll set technical direction for Dev Platform ranking, raise the bar on relevance and system reliability, and mentor other engineers as we scale a team of builders who value impact, personal growth, openness, and kindness.

Languages: Go, Python, C++ or any object oriented programming language

Libraries: Baseplate, GraphQL

Databases: Redis, Postgres, Memcached 

Tools: Kubernetes, AWS

Tools: Kubernetes, Docker, AWS, GCP

What You’ll Do

• Design and ship next-generation ranking and personalization models that power discovery of Dev Platform apps and games across Reddit surfaces.
• Partner with ML, DS, and product to define signals, APIs, and feedback loops for building, deploying, and iterating on ranking models.
• Explore and productionize new retrieval, ranking, and experimentation approaches that unlock new Dev Platform experiences.
• Champion model quality, reproducibility, and experimentation best practices — including offline evaluation, A/B testing, and monitoring for model health and degradation
• Mentor engineers and lead technical discussions, shaping the long-term ranking and personalization modeling strategy for Dev Platform.

Who You Might Be

• 8+ years of experience building and deploying ML models in production, particularly in ranking or personalization modeling, large-scale representation learning, or recommender systems.
• Proven track record working on cross-functional product teams (PM, Design, DS, Eng) where you owned end-user outcomes, not just models or infra, and shipped features that moved core product metrics.
• Strong domain knowledge in modern ranking and personalization techniques: user/item embeddings, multi-task learning, sequential modeling, or similar approaches applied at scale.
• Experience designing and implementing performant, stable, and efficient ML or ranking systems (recommendation, ads, search, feed, or similar high-throughput decision systems).
• Strong organizational skills with the ability to prioritize, sequence, and de-risk work, keeping complex projects on schedule with a high attention to detail.
• BS in Computer Science or a related technical field, or equivalent practical experience.
• Comfortable with software engineering best practices: testing, code reviews, technical design docs, and clear documentation for other teams that depend on your systems.
• Entrepreneurial mindset: you are self-directed, comfortable in ambiguity, and biased toward action in fast-paced environments. You like 0→1 building, iteration, and learning from experiments and failures.
• Excellent communication skills: you collaborate effectively in a remote, cross-functional team, and can explain complex ML and ranking concepts to both technical and non-technical stakeholders.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave  

#LI-remote

We’re looking for a Senior Staff Machine Learning Engineer to lead Reddit’s next-generation user understanding initiative: building a unified, high-fidelity representation of each user that powers personalization across the platform.

This role requires deep expertise in mainstream ML user modeling approaches (e.g., large-scale embeddings, user interest modeling, affinities, behavioral signals) and the ability to reimagine these systems in the GenAI era—leveraging LLMs and foundation models to unlock step-change improvements in fidelity, adaptability, and expressiveness.

You will set the technical direction for this space, leading the design and implementation of Reddit’s core user representation layer—spanning embeddings, interest modeling, and key user attributes. You’ll ensure this foundation is scalable, reliable, and widely adopted across Feeds, Search, Notifications, and Ads, partnering closely with product, infrastructure, and downstream ML teams to drive measurable impact.

This is a high-impact role. The systems you build will shape how hundreds of millions of people experience Reddit every day—what they see, what they discover, and the communities they connect with. Your work will directly advance personalization and relevance at global scale, strengthening Reddit as a platform for meaningful connection and belonging.

What you'll do:

• Design User Understanding Strategy: Define a unified user understanding framework and strategy: how users are represented (embeddings, tags, attributes, LLM-based user profile), how they are computed, stored, and exposed. Provide thought leadership in user understanding and user modeling by setting a long-term technical vision and advancing the state-of-the-art in the field.
• Build Foundational User Models: Lead design and implementation of advanced user models, e.g. large-scale user representation learning (sequence-based, multi-interest, multi-task) that share representations across surfaces to improve personalization experience across key Reddit products e.g. Feeds, Notification, Search and Ads, balancing latency, cost, and performance.
• Reimagine user understanding with LLM/Gen-AI: Evolve user modeling beyond traditional representations by leveraging LLMs to build richer user understanding (e.g., dynamic user profiles, intent inference, semantic reasoning over user behavior). Explore how LLMs can augment or unify embeddings, attributes, and taxonomies to enable more adaptive, interpretable, and context-aware personalization.
• Ship Large Scale User Understanding as a System: Partner with platform teams to design and build core components for large-scale learning and serving: storage/retrieval for embeddings, feature pipelines, and APIs. Collaborate with ML/Ranking infra to ensure low-latency serving, high availability, and integration with MLOps systems.
• Drive Cross-Team Integration & Impact: Partner with Feeds, Notification, Search and Ads teams to drive experimentation and adoption of new user understanding models with product teams across Reddit, ensuring measurable end-to-end impact on key metrics.
• Set Technical Bar & Mentor: Mentor senior to staff engineers, lead design reviews, steward technical decisions across the user understanding domain, and champion and drive engineering processes and best practices

Who you might be:

• You have at least 10 years experience building and scaling production-grade ML systems, particularly in user modeling, large-scale representation learning, or recommender systems.
• You have a track record of driving ambiguous, high-impact initiatives from concept to production, shaping both technical direction and execution.
• You are product- and impact-oriented: you care deeply about how your work moves real metrics (e.g., engagement, retention, revenue), not just model quality.
• You bring strong fundamentals in mainstream user understanding ML approaches (e.g., representation learning, behavioral modeling, user clustering), and understand their trade-offs in real-world systems.
• You are excited about the GenAI shift and have experience (or strong intuition) applying LLMs or foundation models to evolve existing systems,  going beyond incremental improvements.
• You think in systems, not just models: you consider data, training, evaluation, serving, and adoption as a cohesive whole, and design with end-to-end impact in mind.
• You influence beyond your immediate team: partnering effectively with product, infra, and other ML teams, and driving alignment across multiple stakeholders.
• You raise the technical bar: mentoring senior engineers, leading design reviews, and establishing best practices for building reliable, scalable ML systems.
• You are comfortable navigating trade-offs across quality, latency, cost, and safety, especially in large-scale, user-facing systems.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

#LI-remote, #LI-JS5

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 Machine Learning Engineer, you will lead the development of a large-scale ML Inference Platform at Reddit.

• Lead the end-to-end design, implementation, and maintenance of a highly available, low-latency GPU-based model serving system for search, ranking, and LLMs supporting Millions of QPS.
• Design and develop ML and Generative AI systems in cloud-based production environments on Kubernetes at scale.
• Rapidly develop prototypes and develop a high-performance feature hydration and processing system as a part of the inference stack - including routing, caching, and batching.
• Lead a unified GPU model export framework to support converting trained models into optimized GPU inference models.
• Strong understanding of real-time ML observability to track feature/model performance.
• Experience working with LLM serving online at scale.
• Built an E2E inference performance benchmarking framework
• Deep Understanding of multi-cluster compute environment and network topology that is specific to ML inference use cases.

Who You Might Be:

• 7+ years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment roles.
• Have experience operating orchestration systems such as Kubernetes at scale
• Deep experience with cloud-based technologies for supporting an ML platform, including tools like AWS, Google Cloud Storage, infrastructure-as-code (Terraform), and more
• Proficiency with the common programming languages and frameworks of ML, such as Go, Python, etc.
• Excellent communication skills with the ability to articulate technical AI concepts to non-technical stakeholders
• 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 genAI product development lifecycle.
• Strong knowledge of model serving, inference pipelines, monitoring, and observability for AI systems is a plus
• Strong proficiency in Python and deep experience with modern AI/ML frameworks (Triton, Dynamo, vLLM, Pytorch)

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

The GenAI Security team within Reddit’s Security, Privacy, Assurance, and Corporate Engineering organization protects Reddit’s GenAI usage across employee tools, internal agents, and production user-facing systems. Our mission is to secure and protect Reddit’s AI traffic and GenAI adoption by default.

We are building zero-trust, defense-in-depth systems that verify identity, permissions, data access, and semantic intent across AI workflows. A core part of this work is developing practical, high-quality ML models that detect and prevent security risks such as prompt injection, jailbreak attempts, sensitive data exfiltration, unsafe model behavior, anomalous usage, and unauthorized agent actions.

We are looking for a Senior Machine Learning Engineer to lead model development for GenAI Security and help establish strong ML practices across SPACE. This role owns the full machine learning lifecycle: problem definition, data ETL, feature engineering, model training, model evaluation, deployment, experimentation, prediction, monitoring, debugging, and retraining.

What You’ll Do

• Build and improve security-focused ML models for Reddit’s GenAI traffic, including guardrail models, semantic classifiers, anomaly detection models, and other neural network based security signals.
• Own model development end to end: define the security problem, assemble and label datasets, build ETL pipelines, engineer features, train models, evaluate quality, deploy to production, monitor performance, and retrain from production feedback.
• Use modern deep learning architectures, including neural networks, transformers, sequence models, embeddings, and model distillation where they are the right practical fit.
• Design rigorous evaluation suites for adversarial examples, hard negatives, long-context inputs, structured payloads, tool calls, multi-turn workflows, and real production traffic.
• Improve model precision, recall, latency, cost, calibration, and operational reliability for high-impact production surfaces.
• Build repeatable MLOps workflows for SPACE, including training pipelines, model lineage, artifact management, holdout evaluation, dashboards, rollback paths, and retraining loops.
• Partner closely with ML Infrastructure, LLM Gateway, DevX, Ads, Answers, Safety, Privacy, Compliance, and other Security teams to bring security models into real production workflows.
• Work pragmatically with Reddit’s evolving ML platform, using existing infrastructure where possible and building focused tooling when needed to keep model iteration moving.
• Translate security goals into measurable model outcomes and help partners understand tradeoffs between risk reduction, latency, false positives, and product impact.
• Provide technical direction to other engineers and serve as a go-to ML expert for GenAI Security and broader SPACE model needs.

Who You Might Be

• 5+ years of experience building, training, evaluating, and deploying production ML or deep learning models.
• Hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, or similar.
• Strong practical understanding of the full ML lifecycle: problem definition, data ETL, feature engineering, training, evaluation, deployment, monitoring, debugging, and retraining.
• Experience building data pipelines and working with large-scale datasets.
• Experience designing rigorous model evaluations, including precision/recall/F1, false positive analysis, threshold tuning, calibration, holdout sets, regression tests, and production-quality validation.
• Experience shipping production-quality software, preferably in Python and/or Go.
• Strong communication skills and ability to explain model behavior, risk tradeoffs, and technical decisions to cross-functional partners.
• BS degree in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.
• Experience in the following areas is a plus:
  • Applying ML to security, privacy, trust and safety, abuse prevention, adversarial ML, or GenAI security problems.
  • Training or fine-tuning neural text models for complex inputs such as long-context prompts, structured payloads, code-like content, multi-turn interactions, or tool calls.
  • Production MLOps or model serving systems such as Airflow, Ray, MLflow, Triton, ONNX, Kubernetes, or similar.
  • Improving model quality through labeling strategy, hard-negative mining, synthetic data generation, distillation, or active learning.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave  

#LI-remote

Location:
This role is completely remote-friendly. 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. 

Team Description:
The Search & Recommendation Relevance team focuses on delivering the most relevant results when users search for anything on Reddit. Our systems and algorithms operate on the world's largest corpus of human conversation, showcasing the best answers and diverse opinions from all across Reddit on any topics -  whether it's recommendations for the best hiking trail, travel advice, or reviews of the next product or restaurant. To achieve this, our Search Recommendation systems need to be built for maintainability, scalability, and low latency in mind.  

Responsibilities:

• Own pipelines and DAGs that move data, features, embeddings, and models through the ML lifecycle
• Design/maintain ranking and retrieval services that run models in real-time
• Build scalable model-serving APIs, ensuring reliability, efficiency, and performance
• Create reusable infrastructure that other MLEs depend on to train, deploy, and iterate on models
• Ensure pipelines and systems support high scale, low latency, and operational excellence
• Enable modeling with better systems, features, and deployment pathways

Qualifications:

• 8+ years of industry experience with a focus on search and recommendation systems.
• 6+ years of  experience in designing, building and iterating large-scale search relevance and infrastructure systems, handling end-to-end system development.
• Proven track record in delivering large and complex systems with big business impacts. 
• Knowledge and experience working with search systems (e.g. Lucene, Solr, ElasticSearch, Opensearch etc.).
• Demonstrated expertise at cross-functional collaboration - successfully shipped several large-scale projects with complex dependencies across teams.
• Proficient in object-oriented programming (Python, Golang).
• Experience in API design and integration with GraphQL, REST, HTTP, Thrift or gRPC.
• Experience of developing applications using large-scale data stack - e.g. Kubeflow, Airflow, BigQuery, Kafka, Kubernetes, Redis etc.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave  

#LI-DB1 #LI-Remote

The Search Recommendations & Relevance team at Reddit is building the next generation of search - one that goes beyond “10 blue links” to deliver real answers, perspectives and conversation from the world's largest corpus of human conversation. We power both Reddit Search and our AI-native Search experience, redefining how users discover and interact with information.

Redditors produce the most amazing content about every niche topic in the world – this underleveraged data coupled with modern advances in AI systems is presenting a unique opportunity to quickly build a new kind of search engine. As a Senior Staff, you will design and build a large-scale system that powers end-to-end search relevance at scale. You’ll  work across multiple areas of the stack, including query understanding, retrieval, ranking, features, measurement, LLM-based answers, RAG etc, partnering deeply with product, org leads, and other XFN to deliver reliable, high quality systems that can help Reddit Search scale to billions of users.

What You’ll Do

• Contribute to advancing Reddit's Search and Recommendations products by designing AI-driven Search experience prioritizing seamless and delightful user experience. 
• Deeply understand the Reddit search product and drive the vision for the search relevance team.
• Enhance core search retrieval and ranking, design and implement new search engine features,  build and scale search indexes, develop and test new pipeline components.  You will also deploy  ML models, integrate LLMs, and ensure robust monitoring and smooth product integration throughout the process.
• Collaborate across disciplines and with ML, Product, Infrastructure, and DS teams at Reddit to find technical solutions to complex challenges.
• Mentor and guide senior and staff engineers in the team.
• Influence org-wide technical direction and partner closely with senior leadership to shape the long-term roadmap

Who You Are

• 10+ years of industry experience with deep expertise in large-scale search and recommendation systems. 
• Proven ability to identify key opportunities, define roadmaps and drive scalable improvement in  search relevance.
• Strong experience in building and deploying large-scale ML models using frameworks such as PyTorch or Tensorflow.
• Experience working with LLM in production, including evaluation, tuning and deployment.
• In-depth knowledge and experience working with lexical and semantic retrieval systems.
• Proficiency in object-oriented programming (Python, Golang).
• Nice to have: experience building production Agentic AI frameworks.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

#LI-remote, #LI-JS5

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 Senior Software Engineer, Machine Learning Platform (Training Platform), you will be instrumental in architecting, implementing, and maintaining foundational Machine Learning (ML) infrastructure that powers Feeds Ranking, Content Understanding, Recommendations and much more to fulfill Reddit’s mission of bringing community and belonging to everyone in the world.  You will deliver a self service ML platform that enables the continuous iteration and improvement of systems that use ML techniques including Deep Learning, Natural Language Processing, Recommendation Systems, Representation Learning and Computer Vision.

• Lead the building, testing, and maintenance of ML training infrastructure at Reddit.
• Play a pivotal role in designing, building, and optimizing the infrastructure and tooling required to support large-scale machine learning workflows.
• Evolve the MLE experience, from provisioning interactive GPU environments through large-scale training, supporting on-demand and self-service workflows.
• Kubernetes Automation: Write custom Kubernetes Controllers and Operators to manage the lifecycle of interactive Jupyter workspaces and long-running ML training jobs, handle auto-idling, and ensure fault tolerance.
• GPU Orchestration: Work with the underlying compute team to ensure MLEs have efficient access to training hardware resources and handle resource contention gracefully.
• Developer Experience (DevX): Treat internal MLEs as your customers. Conduct user research, reduce friction in the "Idea-to-Prototype" loop, and standardize software environments (Docker images, Python dependency management).

Who You Might Be:

• 5+ years of software engineering experience, with a focus on Platform Engineering, ML Infrastructure, or Backend Systems.
• Deep Kubernetes Expertise: You know K8s beyond just "deploying pods." You understand CRDs, Controllers and the Operator pattern.
• Jupyter Ecosystem Knowledge: Experience customizing JupyterHub, JupyterLab extensions, or building similar interactive computing platforms.
• Strong Coding Skills: Proficiency in Python (for the ML ecosystem) and Go (for Kubernetes controllers/infrastructure tooling).
• GPU Experience: Hands-on practice with CUDA environments, GPU virtualization/containerization, and doing it all within Kubernetes.
• Cloud Provider Experience: Familiarity with both managed ML offerings (Vertex AI, Sagemaker, etc) and building custom ML components in AWS and/or GCP.
• Experience working with distributed training frameworks, including Ray and Kubernetes.
• Comfortable with distributed systems, big data (Petabyte scale) and data-intensive systems.
• 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.

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

#LI-Remote

Who We Are:
The Machine Learning Platform team at Reddit is a high-impact team that owns the foundational embeddings and 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 Senior Engineering Manager, Unified Embeddings Platform, you will work lead our Embeddings Platform Team and be instrumental in architecting, implementing, and maintaining platform providing foundational ML embeddings  based on state of the art architectures such as large scale graph neural networks, foundational models, large scale sequence models, etc.  empowering all product verticals at Reddit to fulfill Reddit’s mission of bringing community and belonging to everyone in the world. You’ll partner with teams across Reddit infrastructure and ML modelling at the center of machine learning to enable machine learning engineers (MLEs) and data scientists (DSs) to improve the ML feature software development lifecycle.

• Lead the creation of foundational embeddings at Reddit leveraging state of the art architectures such as graph neural networks, transformer based sequence models, foundational models, etc.
• Lead the design and long-term evolution of Reddit’s Embeddings platform enabling all ML teams access, store and share embeddings
• Drive high-impact, high-leverage projects that align with Reddit’s broader engineering and business goals
• Build and grow a high-performing team, including hiring, mentoring, and creating a culture of technical excellence
• Establish and champion best practices for the production, consumption, and governance of machine learning features
• Serve as a thought partner to product and upper management to ensure your team’s plans align with company goals.
• Communicate your team’s work and set expectations with cross-functional stakeholders.

Who You Might Be:

• 4+ years of experience in people management
• 8+ years of work experience in a production software development environment
• 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.
• Experience working on large-scale ML Systems serving 100s of millions of users
• Experience building self service platform enabling storage and sharing of large scale data and embeddings
• Experience with leading modelling teams powering the creation of foundational embeddings based on state of the art architectures
• Strong organizational & communication skills

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave  

Team
The ML Indexing & Retrieval Platform team at Reddit is responsible for building and scaling the core infrastructure that powers machine learning driven recommendations. We design and maintain systems for ML data ingestion, low-latency retrieval services, and end-to-end lifecycle management of data. With a focus on performance, reliability, and scalability, we enable real-time access to high-quality data that supports a wide range of applications, including Content Understanding, Semantic, Lexical retrieval & GenAI applications. 

How You'll Have Impact

You’ll lead the development of next-generation ML Indexing & Retrieval systems, owning the full lifecycle from ideation to production and going beyond incremental improvements to reimagine core platform capabilities. As part of a high-impact, cross-functional team, you’ll solve complex technical challenges to build scalable, reliable platforms that empower developers to efficiently ship critical ML features.

Languages: Go, Java, Python, or any object oriented programming language

Frameworks: Flink, Airflow, Spark for large scale batch & stream processing 

Databases: Familiarity with Vector, Lexical & Key-Value Databases 

Tools: Kubernetes, Docker, AWS, GCP

What You’ll Do

• Lead the technical strategy, architecture, and implementation of Reddit’s next-generation ML Indexing & Retrieval engine, integrating capabilities across lexical and vector indexing, low-latency retrieval, and emerging GenAI applications.
• Partner closely with product engineers across Content Understanding, Search, Feeds, Ads, Growth, and Safety to deliver high-quality experiences.
• Define best practices for observability, reliability, and operational excellence in large-scale distributed systems.
• Mentor and guide engineers in designing scalable infrastructure and adopting robust DevOps and SRE principles.
• Collaborate with infrastructure, and ML teams to ensure the platform evolves to meet the needs of Reddit’s growing user base and diverse content ecosystem.

Who You Might Be:

• 10+ years of experience in software engineering, specializing in Indexing and Retrieval systems.
• 3+ years in technical leadership, architecting and scaling distributed systems in production environments.
• Deep expertise in large-scale data platforms, including batch indexing and stream processing.
• Proven experience designing and operating large-scale, low-latency retrieval services.
• Expertise in lexical and vector search retrieval technologies, such as Milvus, Vespa, or Elasticsearch. 
• Skilled in designing cloud-native architectures and managing containerized workloads using Kubernetes and AWS/GCP.
• Adept at translating complex technical challenges into clear, actionable strategies.
• Strong communicator and mentor who leads through collaboration, influence, and technical excellence.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

#LI-Remote

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 Senior 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:

• 5+ 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

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

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.

What You’ll Work On

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

What You’ll Do

• Lead end-to-end ML initiatives from ideation through production and iteration, shaping technical direction and translating product goals into scalable solutions  
• Architect, build and deploy large-scale ML systems across recommendation, search, and content/user understanding, including retrieval/ranking models, representation learnings embeddings optimizations, and LLM or GenAI-powered capabilities  
• Drive measurable impact on user engagement, discovery, and long-term value
• Collaborate with cross-functional teams to align product and technical roadmaps and unlock key future ML capabilities
• Stay at the forefront of AI research, evaluating and introducing new AI/ML paradigms to keep Reddit’s ML ecosystem at the cutting edge
• Contribute to the development of best practices, guidelines, and ethical AI principles for responsible LLM development and deployment
• Mentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharing
• Set technical vision and drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-making

Required 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

Preferred Qualifications

• Subject matter expertise in one of the following domains:
• Recommender systems
• 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

Potential Teams

• Home Experience
• ML Understanding
• Feed Relevance
• Answer Experience
• Search and Answers Relevance
• Search Experience

Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

#LI-Remote