Senior Machine Learning Engineer, Relevance and Personalization
Build and productionize cutting-edge ML models for Airbnb's query intelligence, including autocomplete, query tagging, expansion, intent modeling, and LLM-powered natural language search to understand guest intent.
About the job
Responsibilities
- Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases, with a focus on query understanding.
- Develop query understanding capabilities — autocomplete and smart compose, query tagging (sequence tagging / NER), query expansion, and query/user intent modeling — and natural-language ("search in your own words") search experiences powered by modern NLP and LLMs.
- Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.
- Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
- Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.
Example projects include: smart compose and language generation for search, LLM-based sequence taggers, LLM-driven query/location expansion, intent classification, and user-intent sequence modeling.
Requirements
- 5+ years of industry experience in applied Machine Learning, inclusive of MS or PhD in relevant fields.
- Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.
- Deep understanding of Machine Learning best practices (e.g. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (e.g. neural networks/deep learning, optimization) and domains (e.g. natural language processing, personalization, search and recommendation, marketplace optimization).
- Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (e.g. Hive).
- Industry experience building end-to-end Machine Learning models.
- Experience applying large language models and modern NLP — e.g., sequence tagging/NER, text generation, intent classification, or embedding/representation learning.
- Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).
Nice-to-Haves
- Familiarity with building natural-language, AI-native and agentic search experiences is a plus.
Skills
Machine Learning, NLP, LLMs, PyTorch, TensorFlow, Python, Scala, Java, Spark, Kubernetes, Airflow, Kafka, Query Understanding, Intent Classification, Ner
Similar jobs
ML Engineering jobs
Traba
New York, NYBuild and deploy production AI-agent systems, including their harnesses, evaluations, orchestration, and supporting services. The role requires 5+ years of software engineering experience, production LLM or agent experience, and strong Python or TypeScript/Node.js skills.
Metriport
San Francisco, CA
Own machine learning end to end, from modeling messy clinical data through production deployment, monitoring, and infrastructure. The role requires 7+ years of experience building scalable ML systems, strong software and data engineering skills, and proficiency with Python, SQL, and cloud platforms.
SentiLink
United States
Leads and manages an applied machine learning team developing production fraud detection and identity verification models. The role combines people leadership with hands-on technical work and requires substantial ML experience, production deployment expertise, and experience in risk-focused domains.
Airbnb
United States
Builds and productionizes machine learning systems for trust and safety, including abuse detection, autonomous AI agents, and evaluation frameworks. The role requires 5+ years of applied ML experience, strong Python skills, experience with LLMs and scalable pipelines, and a relevant advanced degree or equivalent background.
6sense
San Francisco, CA
Owns end-to-end production machine learning systems, including NLP, LLM, agentic, ranking, and recommendation capabilities. Requires 8+ years of industry experience, strong Python and cloud ML expertise, and the ability to deliver explainable AI products with cross-functional and customer impact.