Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. It takes a unified team committed to our core values to achieve this goal. Airbnb's various functions embody the company's innovative spirit and our fast-moving team is committed to leading as a 21st century company.
What does being an Machine Learning Engineer look like:
As a Machine Learning Engineer you are eager to understand complex systems top to bottom and thrive working across technologies and codebases. Your contributions take a variety of shapes:
- 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.
- 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: feature platform, model interpretability, hyperparameter optimization, concept drift detection.
Who are we looking for:
- 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. gradient boosted trees, neural networks/deep learning, optimization) and domains (eg. natural language processing, computer vision, personalization and recommendation, anomaly detection)
- 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 infrastructure and/or building and productionizing Machine Learning models
- Exposure to architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
- Experience with test driven development, familiar with A/B testing, incremental delivery and deployment.
The starting base pay for this role is between $184,000 and $244,000. The actual base pay is dependent upon many factors, such as: education, experience, and skills. 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.
This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. Airbnb,Inc. can employ in states where we have registered entities. Currently, employees can not be located in: Alaska, Indiana, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin, Alabama, Mississippi, Oklahoma, Delaware and Rhode Island. This list is continuously evolving and being updated, please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from.
Belonging & Accommodations
Airbnb is committed to working with the best and brightest people from the broadest talent pool possible. We believe a diversity of ideas fosters innovation and engagement, and allows us to attract the best people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. If you need assistance, or a reasonable accommodation during the application and recruiting process, please contact us at: email@example.com.