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Staff Machine Learning Engineer – Customer Support Engineering
| Company | Airbnb |
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| Location | United States |
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| Salary | $204000 – $255000 |
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| Type | Full-Time |
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| Degrees | PhD |
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| Experience Level | Senior, Expert or higher |
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Requirements
- PhD in Computer Science, Mathematics, Statistics, or related technical field
- 7+ years of experience in building, testing and shipping AI models and products from inception to production; including 2+ years of experience with GenAI
- 5+ years experience leading and guiding applied science/machine learning teams that deliver large impact as a senior IC
- Deep knowledge and hands-on experience with 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. NLP, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection)
- Experience with AI technologies in customer support applications
- Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain
- Experience building robust testing frameworks for agent behavior validation and continuous improvement, and driving architectural requirements on ML infrastructures
- Ability to absorb new concepts quickly and integrate them effectively into business processes.
Responsibilities
- 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 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.
Preferred Qualifications
No preferred qualifications provided.
