Staff Machine Learning Engineer, Search Ranking

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, 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

Zone A (CA, WA, NYC) : The base salary range for this position is $229,000-$343,000 annually.

Zone B : The base salary range for this position is $218,000-$326,000 annually.

Zone C : The base salary range for this position is $195,000-$292,000 annually.