Lead measurement, detection, and enforcement strategy for ads fraud at Reddit: define taxonomies and metrics, analyze large behavioral datasets, and build scalable detection/enforcement pipelines (features, models, rules, scoring, feedback loops). Requires an M.S. + 4+ years or Ph.D. + 2+ years of industry data science experience and fluency with Python, SQL, statistical analysis, fraud-detection methods, and production ML systems; experience with LLMs, graph analysis, and streaming/batch pipelines is valued.

Senior Staff ML Systems Engineer responsible for architecture and technical strategy across feature platforms, training-data systems, offline experimentation, and ML development workflows. Role requires partnering with Ads, ML Platform, Data Platform and engineering teams, mentoring senior engineers, and shipping platform abstractions to speed and harden ML development. Requires 8+ years in infrastructure/distributed systems and 4+ years building or operating production ML infrastructure; mentions experience with Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, or Databricks.

Senior Software Engineer on Reddit’s Ads Creative Effectiveness team responsible for architecting and implementing scalable backend systems and pipelines for generative ad creative (image, video, copy), model serving and orchestration, performance feedback integration (CTR/CVR), safety/compliance middleware, and mentoring engineers. Remote — United States. Base salary range: $190,800–$267,100 USD/year.

Senior technical lead role owning the design and execution of large-scale ML infrastructure for Reddit's embedding and recommendation systems. Responsibilities include defining ML strategy, building distributed training across GPUs/cloud, optimizing low-latency real-time serving for embeddings, integrating models with product teams, and mentoring engineers. Requires 8+ years in machine learning engineering with expertise in deep learning, recommendation/personalization systems, Python/C++ and ML infrastructure.

Expect to own end-to-end ML components for recommendation and personalization: model design, data preparation, training, evaluation, and production deployment. Role emphasizes distributed training and inference, sequence and foundation-model techniques, low-latency/high-throughput serving, monitoring, and A/B-driven evaluation.

Lead and grow a team of ML engineers to define technical vision and deliver large-scale production ML solutions (including transformer/sequence/LLM models) for feed relevance and media ranking. Partner cross-functionally, maintain operational excellence, and drive hiring and team development. Requires 5+ years leading ML teams.

Remote (United States) Senior Staff ML Engineer role at Reddit to lead development of GenAI-powered recommendations and core feed ranking architectures. Responsibilities include setting technical direction across Relevance and Feeds teams, delivering high-impact initiatives, and mentoring senior engineers. Qualifications require 10+ years industry experience in relevance/recommendation and large-scale ML systems. Base salary range provided: $266,000–$372,400 USD per year.

Reddit is hiring a Senior Machine Learning Systems Engineer to build scalable offline ML experimentation platforms, production training orchestration, experiment tracking, model registries, and agentic AI execution infrastructure for Ads ML. The role requires experience in large-scale distributed systems, ML platform engineering, and workflow/orchestration technologies (e.g., Kubeflow, Argo, Airflow, Spark/Flink/Ray).

Lead technical strategy and execution for models powering Reddit Shopping Ads (Dynamic Product Ads, Product Listing Ads). Own end-to-end model work including data and label design, feature engineering, model selection, offline and online evaluation, deployment, monitoring, and iteration. Require substantial experience building large-scale, low-latency ML systems, ads or recommendation model experience, and demonstrated technical leadership/mentoring.

Lead a team of machine learning engineers responsible for sourcing and shaping candidate inventory for Reddit's personalized feeds. Responsibilities include defining technical vision and roadmap for Feed Retrieval, overseeing design and optimization of retrieval systems, establishing measurement and experimentation practices, collaborating with ML platform and product teams, maintaining system performance and reliability, and recruiting/hiring. Qualifications include 2+ years managing ML or recommender-systems teams and hands-on experience with large-scale production ML (recommenders, embeddings, transformers/LLMs). Base salary range listed: $253,300–$354,600 USD per year.

Manage the Feed Ecosystems team to define technical vision and roadmap for recommender-system investments; oversee design and delivery of ML systems for cold-start relevance, post/community distribution, and feed quality; partner with product, design, data science, safety, ads, and platform teams; and hire and develop ML engineering talent. Requires 2+ years leading ML or recommender-systems teams and hands-on experience with large-scale production ML.

Senior technical role owning measurement, identity resolution, and signal strategy for Reddit Ads. Responsibilities include designing probabilistic identity models, owning lift and experimentation methodologies, quantifying and incorporating new signals into predictive models, and defining evaluation frameworks. Requires an advanced degree (MS or PhD) with significant industry experience, strong statistical programming (Python or R) and SQL skills, and expertise in measurement, experimentation, and predictive modeling.

Senior MLE (IC4) role on Reddit's Ads Optimization team responsible for designing and implementing optimization algorithms for auctions, bidding strategies, and budget pacing; owning end-to-end systems from problem formulation through production deployment and experimentation. Requires strong ML and software engineering experience with scalable data processing and real-time/low-latency systems.

Lead and grow an ML-engineering/systems team focused on model optimization, training and inference efficiency, GPU enablement, profiling, benchmarking, load testing, observability, cost analysis, and efficiency certification. Partner with ranking, ML Platform, and serving teams to deliver measurable reductions in training time, latency, and serving cost. Requires deep ML engineering experience, distributed-systems fluency, and strong managerial skills. Base salary range (US): $230,000–$322,000 USD.

Join Reddit’s ML Efficiency team to design and build systems, tooling, and optimization for ML training, evaluation, deployment, and operation at scale. Work areas include distributed training and serving, GPU and resource utilization, profiling and benchmarking, data pipelines, performance dashboards, and cross-functional initiatives to reduce costs and accelerate experimentation. Role requires strong software engineering experience (5+ years), Python proficiency, and systems-language experience and knowledge of ML infrastructure and performance engineering.

Join Reddit's ML Efficiency team to design and build systems, tooling, and optimizations that improve ML training and inference efficiency at scale. Responsibilities include improving GPU and resource utilization, developing profiling/debugging tools, optimizing distributed training and serving, building benchmarking frameworks and dashboards, and leading cross-functional initiatives. Requires BS/MS/PhD in CS or related field, 5+ years of software engineering experience, strong Python proficiency, and experience with distributed systems and ML infrastructure.

Lead technical direction and hands-on development (approx. 50% leadership/mentorship, 50% individual contributor) of content- and behavior-based embeddings and representation systems for ads, including multimodal embeddings, sequence models, knowledge-graph embeddings, and LLM/VLM-based representations. Requires 7+ years experience in end-to-end ML model lifecycle, strong background in content-rich NLP/CV at scale, familiarity with recommender/search/ads, and experience with frameworks such as PyTorch or TensorFlow.

The role leads technical strategy and execution for Ads Foundational Representations: building embeddings (text, media, knowledge graph, user intent) and related data and inference pipelines, working with LLMs/VLMs, and owning evaluation and experimentation. Requires 7+ years of ML lifecycle experience, staff-level technical leadership, strong experience with content-rich NLP/CV at scale, and familiarity with recommender/search/ads systems.

Staff Machine Learning Engineer on the Notifications Relevance team responsible for designing and implementing large-scale recommendation systems (retrieval, ranking, budget optimization), integrating LLMs, deploying models to production, and ensuring monitoring and product integration. Requires 8+ years of industry experience, strong experience with PyTorch or TensorFlow, production LLM work, and proficiency in Python and Golang. Salary range listed: $230,000–$322,000 USD per year.

Join Reddit's ML Ranking Platform to design, implement, and maintain large-scale, performant ML ranking and recommendation systems that power personalized feeds, search, and other products. Requires 5+ years building distributed, data-intensive ML systems; experience with Go, Python, C++, cloud production environments, ML monitoring, deployment/automation, and recommendation systems is preferred.