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Senior Staff Machine Learning Engineer, Growth Platform Engineering
Location
United States
Posted
48 days ago
Salary
$244K - $305K / year
Seniority
Senior
Job Description
Senior Staff Machine Learning Engineer, Growth Platform Engineering
Airbnb
• As a machine learning engineer or scientist, your expertise will be pivotal in developing AI-powered solutions to shape the future of the Airbnb agentic growth platform with cutting-edge AI techniques. You will drive and guide the rest of the engineers to brainstorm, design and develop AI products and features from inception to production. • Collaborate with cross-functional leaders, build resilient systems that operate globally at scale, and help evolve the foundational building blocks behind AI-powered growth systems. • Work with large scale structured and unstructured data; explore, experiment, build and continuously improve Machine Learning models and pipelines for Airbnb product, business and operational use cases. • Work collaboratively with cross-functional partners including product managers, operations and data scientists, to identify opportunities for business impact; understand, refine, and prioritize requirements for machine learning, and drive engineering decisions. • Hands-on develop, productionize, and operate ML/AI 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. • Collaborate actively with engineers to apply ML / AI in their solutions to help validate ideas and guide to the right outcomes. • Partner with ML/AI Engineers in foundations engineering to mentor and develop initiatives that make ML/AI applications a core discipline for non-ML/AI engineers.
Job Requirements
- 12+ years of industry experience in applied ML/AI, inclusive MS or PhD in relevant fields
- Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills
- Deep understanding of ML/AI 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, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection)
- Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection) and algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization)
- Experience with technologies such as: Tensorflow, PyTorch, Kubernetes, Airflow (or equivalent), Kafka (or equivalent)
- Expertise with architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
- Agentic and Automation: Experience with AI technologies in automating processes and developing agentic solutions and frameworks.
- Agile Practice for AI Production: Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain.
- Infrastructure Acumen: Experience building robust testing frameworks for agent behavior validation and continuous improvement, and driving architectural requirements on ML infrastructures.
Benefits
- This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.
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Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position. The University of Chicago's Annual Security & Fire Safety Report (Report) provides information about University offices and programs that provide safety support, crime and fire statistics, emergency response and communications plans, and other policies and information. The Report can be accessed online at: http://securityreport.uchicago.edu. Paper copies of the Report are available, upon request, from the University of Chicago Police Department, 850 E. 61st Street, Chicago, IL 60637.



