About the job
Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services. Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront. We’re looking for a Staff Machine Learning Engineer to join Snap Inc! We are looking for a Staff Machine Learning Engineer to lead the development of next-generation Search ranking systems. In this role, you will design, build, and improve machine learning models that determine the relevance, quality, personalization, and utility of search results at scale.
Responsibilities
Lead the design and development of machine learning models for Search ranking, including relevance ranking, personalization, result quality, intent understanding, and engagement optimization
Own major ranking initiatives from problem definition through experimentation, launch, and iteration
Develop and improve ranking models using techniques such as learning-to-rank, deep retrieval, neural ranking, sequence models, embeddings, multi-task learning, calibrated prediction, and large-scale feature engineering
Build ranking systems that balance multiple objectives, such as relevance, user satisfaction, freshness, diversity, fairness, safety, latency, and business goals
Partner with product managers, data scientists, and engineers to define success metrics, experimentation strategy, and long-term ranking roadmap
Analyze user behavior, search logs, query-result interactions, and model performance to identify opportunities for improvement
Design robust offline evaluation, online experimentation, and model monitoring frameworks
Improve feature pipelines, training infrastructure, serving systems, and model iteration velocity
Provide technical leadership across teams, influence architecture decisions, and mentor engineers working on ML ranking systems
Stay current with advances in search, recommendation systems, ads ranking, generative AI, LLM-based ranking, and retrieval-augmented systems
Qualifications
Minimum
Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience
8+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 7+ year of post-grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience
Experience developing machine learning models for relevance ranking, personalization, intent understanding, and/or engagement optimization
Experience with large-scale data processing and ML infrastructure, such as Spark, Flink, Beam, TensorFlow, PyTorch, JAX, or similar tools
Preferred
Advanced degree in Computer Science, Machine Learning, Statistics, Mathematics, Information Retrieval, or a related field
Direct experience building Search ranking systems, including query understanding, retrieval, ranking, re-ranking, relevance modeling, or result blending
Experience with ads ranking, recommendation ranking, feed ranking, marketplace ranking, or content discovery systems
Experience with learning-to-rank methods such as LambdaMART, pairwise/listwise ranking losses, neural ranking models, or transformer-based rankers
Experience with candidate generation, retrieval models, ANN search, embeddings, vector search, or two-stage ranking architectures
Experience optimizing ranking systems for multiple objectives, including relevance, engagement, quality, diversity, freshness, long-term user value, and monetization
Experience with LLMs, foundation models, semantic search, natural language understanding, or retrieval-augmented generation
Experience building low-latency ML serving systems and improving production model reliability
Track record of publishing, patenting, or otherwise advancing the state of the art in search, ranking, recommendations, ads, or applied ML