Machine Learning Engineer, Search Ranking

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’s three core products are Snapchat () , a visual messaging app that enhances your relationships with friends, family, and the world;
Lens Studio () , an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles () .

Snap Engineering () teams build fun and technically sophisticated products that reach hundreds of millions of Snap chatters 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.

• 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

• Strong machine learning fundamentals, including supervised learning, ranking models, embeddings, deep learning, optimization, evaluation, and experimentation

• Strong programming skills in Python, C++, Java, Scala, or similar languages

• Experience with large-scale data processing and ML infrastructure, such as Spark, Flink, Beam, Tensor Flow, PyTorch, JAX, or similar tools

• Ability to take ML models from research or prototyping into large-scale production systems

• Strong understanding of online experimentation, A/B testing, metric design, model debugging, and tradeoff analysis

• Proven ability to lead complex technical projects across multiple teams

• Excellent communication skills and ability to explain complex ML concepts to technical and non-technical stakeholders

• 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, Tensor Flow, PyTorch, JAX, or similar tools