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# Staff Machine Learning Engineer, Search Ranking

- **Company:** Snap Inc.
- **Location:** Bellevue, WA, United States
- **Experience:** Experienced
- **Salary:** $229,000.0 - $343,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), A/B Testing, Big Data, C++ (Programming Language), Computer Programming, Software Debugging, Information Retrieval, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Scala (Programming Language), Search Technologies, Supervised Learning, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Deep Learning, Information Technology, Apache Flink, Machine Learning Operations, Natural Language Understanding
- **Published:** July 23, 2026
- **Apply:** https://dejobs.org/x/x/F5C9EA7FB7CC4D86A153F7674D040C57/job/

## About the Role

* 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, TensorFlow, 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, TensorFlow, PyTorch, JAX, or similar tools, * 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

## Description

* 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


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