Staff Machine Learning Engineer, Search Ranking
Snap Inc
Staff Machine Learning Engineer, Search Ranking
Job Description
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.What You’ll DoLead the design and development of machine learning models for Search ranking, including relevance ranking, personalization, result quality, intent understanding, and engagement optimizationOwn major ranking initiatives from problem definition through experimentation, launch, and iterationDevelop 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 engineeringBuild ranking systems that balance multiple objectives, such as relevance, user satisfaction, freshness, diversity, fairness, safety, latency, and business goalsPartner with product managers, data scientists, and engineers to define success metrics, experimentation strategy, and long-term ranking roadmapAnalyze user behavior, search logs, query-result interactions, and model performance to identify opportunities for improvementDesign robust offline evaluation, online experimentation, and model monitoring frameworksImprove feature pipelines, training infrastructure, serving systems, and model iteration velocityProvide technical leadership across teams, influence architecture decisions, and mentor engineers working on ML ranking systemsStay current with advances in search, recommendation systems, ads ranking, generative AI, LLM-based ranking, and retrieval-augmented systemsKnowledge, Skills, & AbilitiesStrong machine learning fundamentals, including supervised learning, ranking models, embeddings, deep learning, optimization, evaluation, and experimentationStrong programming skills in Python, C++, Java, Scala, or similar languagesExperience with large-scale data processing and ML infrastructure, such as Spark, Flink, Beam, TensorFlow, PyTorch, JAX, or similar toolsAbility to take ML models from research or prototyping into large-scale production systemsStrong understanding of online experimentation, A/B testing, metric design, model debugging, and tradeoff analysisProven ability to lead complex technical projects across multiple teamsExcellent communication skills and ability to explain complex ML concepts to technical and non-technical stakeholdersMinimum QualificationsBachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience8+ 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 experienceExperience developing machine learning models for relevance ranking, personalization, intent understanding, and/or engagement optimizationExperience with large-scale data processing and ML infrastructure, such as Spark, Flink, Beam, TensorFlow, PyTorch, JAX, or similar toolsPreferred QualificationsAdvanced degree in Computer Science, Machine Learning, Statistics, Mathematics, Information Retrieval, or a related fieldDirect experience building Search ranking systems, including query understanding, retrieval, ranking, re-ranking, relevance modeling, or result blendingExperience with ads ranking, recommendation ranking, feed ranking, marketplace ranking, or content discovery systemsExperience with learning-to-rank methods such as LambdaMART, pairwise/listwise ranking losses, neural ranking models, or transformer-based rankersExperience with candidate generation, retrieval models, ANN search, embeddings, vector search, or two-stage ranking architecturesExperience optimizing ranking systems for multiple objectives, including relevance, engagement, quality, diversity, freshness, long-term user value, and monetizationExperience with LLMs, foundation models, semantic search, natural language understanding, or retrieval-augmented generationExperience building low-latency ML serving systems and improving production model reliabilityTrack record of publishing, patenting, or otherwise advancing the state of the art in search, ranking, recommendations, ads, or applied MLIf you have a disability or special need that requires accommodation, please don’t be shy and provide us some information."Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success!CompensationIn the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.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.This position is eligible for equity in the form of RSUs.
Locations
- California, Palo Alto
- Washington, Seattle
- California, San Francisco
- California, Los Angeles
- Washington, Bellevue
Salary
229,000 - 343,000 USD / yearly
Skills Required
- large-scale data processingintermediate
- Search ranking systemsintermediate
- ads rankingintermediate
- learning-to-rank methods such as LambdaMARTintermediate
- candidate generationintermediate
- LLMsintermediate
- low-latency ML serving systemsintermediate
Required Qualifications
- Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience (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, 8 years)
- Experience developing machine learning models for relevance ranking, personalization, intent understanding, and/or engagement optimization (experience)
- Experience with large-scale data processing and ML infrastructure, such as Spark, Flink, Beam, TensorFlow, PyTorch, JAX, or similar tools (experience)
- Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience (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, 8 years)
- Experience developing machine learning models for relevance ranking, personalization, intent understanding, and/or engagement optimization (experience)
- Experience with large-scale data processing and ML infrastructure, such as Spark, Flink, Beam, TensorFlow, PyTorch, JAX, or similar tools (experience)
Preferred Qualifications
- Advanced degree in Computer Science, Machine Learning, Statistics, Mathematics, Information Retrieval, or a related field (degree in computer science)
- Direct experience building Search ranking systems, including query understanding, retrieval, ranking, re-ranking, relevance modeling, or result blending (experience)
- Experience with ads ranking, recommendation ranking, feed ranking, marketplace ranking, or content discovery systems (experience)
- Experience with learning-to-rank methods such as LambdaMART, pairwise/listwise ranking losses, neural ranking models, or transformer-based rankers (experience)
- Experience with candidate generation, retrieval models, ANN search, embeddings, vector search, or two-stage ranking architectures (experience)
- Experience optimizing ranking systems for multiple objectives, including relevance, engagement, quality, diversity, freshness, long-term user value, and monetization (experience)
- Experience with LLMs, foundation models, semantic search, natural language understanding, or retrieval-augmented generation (experience)
- Experience building low-latency ML serving systems and improving production model reliability (experience)
- Track record of publishing, patenting, or otherwise advancing the state of the art in search, ranking, recommendations, ads, or applied ML (experience)
- Advanced degree in Computer Science, Machine Learning, Statistics, Mathematics, Information Retrieval, or a related field (degree in computer science)
- Direct experience building Search ranking systems, including query understanding, retrieval, ranking, re-ranking, relevance modeling, or result blending (experience)
- Experience with ads ranking, recommendation ranking, feed ranking, marketplace ranking, or content discovery systems (experience)
- Experience with learning-to-rank methods such as LambdaMART, pairwise/listwise ranking losses, neural ranking models, or transformer-based rankers (experience)
- Experience with candidate generation, retrieval models, ANN search, embeddings, vector search, or two-stage ranking architectures (experience)
- Experience optimizing ranking systems for multiple objectives, including relevance, engagement, quality, diversity, freshness, long-term user value, and monetization (experience)
- Experience with LLMs, foundation models, semantic search, natural language understanding, or retrieval-augmented generation (experience)
- Experience building low-latency ML serving systems and improving production model reliability (experience)
- Track record of publishing, patenting, or otherwise advancing the state of the art in search, ranking, recommendations, ads, or applied ML (experience)
- "Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. (experience)
- Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success! (experience)
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
- 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
- Knowledge, Skills, & Abilities
- 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
- 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
Benefits
- general: In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.
- general: Zone A (CA, WA, NYC):
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