Machine Learning Engineer, Relevance and Personalization (Query Intelligence)

Lead end-to-end execution of packaging category sourcing across assigned markets: manage supplier relationships, run RFP/RFI and contract negotiations, ensure supply continuity and risk mitigation, drive supplier performance and cost optimization, and collaborate with cross-functional teams to deliver category outcomes for the QSR system.

• 5+ years of industry experience in applied Machine Learning, inclusive 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 (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. 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 (eg. 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.
• Familiarity with building natural-language, AI-native and agentic search experiences is a plus.
• Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).

Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits. Pay Range $200,000-$235,000 USD, 5 Hours Ago Remote or Hybrid 139K-205K Annually Senior level 139K-205K Annually Senior level Artificial Intelligence * Cloud * HR Tech * Information Technology * Productivity * Software * Automation Lead territory and account strategy to drive adoption of ServiceNow CRM products for large enterprises. Coach account teams, support solution envisioning and sales cycles, align recommendations with value principles, and partner with specialists to execute digital transformation initiatives. Travel and quota attainment are required. Top Skills: AIAi-Powered ToolsCRMCsmFsmServicenow ServiceNow

Enterprise Account Exec (Enterprise Tech, Cybersecurity)

5 Hours Ago Remote or Hybrid 114K-189K Annually Senior level 114K-189K Annually Senior level Artificial Intelligence * Cloud * HR Tech * Information Technology * Productivity * Software * Automation Sell enterprise SaaS licenses by generating new business through account and territory planning, prospect research, and field sales. Build C-suite relationships, orchestrate account strategies with cross-functional teams, advise customers on IT roadmaps and AI integration, and drive deal negotiation and attainment of sales targets. Top Skills: AIAi AgentsArmisSaaSServicenowVeza

What you need to know about the Colorado Tech Scene

With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.

Key Facts About Colorado Tech

• Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
• Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
• Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
• Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
• Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
• Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products. The summary above was generated by AI

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.

The Community You Will Join:

The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. In this role you’ll focus on query intelligence, the front door of search working on critical, impactful projects that turn what a guest types, taps, or says into a precise understanding of their intent, spanning autocomplete and smart compose, query tagging, query expansion, and intent modeling across Stays, Experiences, and Services.

The Difference You Will Make:

Query understanding is where every search begins, and it directly shapes retrieval, ranking, and ultimately the perfect match between guests and hosts. We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) and increasingly large language models at Airbnb. You’ll build the models that parse free-form and natural-language multimodal queries, extract entities and location context, classify intent, and anticipate what guests want before they finish typing. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb’s mission of creating a world where people can Belong Anywhere. Some past publications from the team can be found here: https://sites.google.com/view/airbnb-relevance-publications/home

A Typical Day:

• 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.