Staff Machine Learning Engineer, Snap Inc.

• 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

Desirable

• 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