Machine Learning Engineer, Relevance And Personalization

at Airbnb

📍 United States

USD 166,000-195,000 per year

MIDDLE

✅ Remote

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 3 API Airflow Algorithms @ 6 Computer Vision @ 6 Data Engineering @ 3 Data Pipelines Deep Learning @ 6 Hive Java @ 6 Kafka Kubernetes Machine Learning @ 3 NLP PyTorch Python @ 6 Scala @ 6 Spark TensorFlow

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.

New grad Ph.D in ML/AI or 2+ years of industry experience in applied ML/AI with a M.S. or B.S degree.
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 (eg. natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection).
Exposure to 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive).
Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).
Proven ability to choose the right ML method to solve the problem within current constraints while having a clear vision of the next iterations and a good balance between exploration and exploitation of different techniques.
Ability to go deep and build the most impactful solutions while also leading multiple directions across multiple teams and organizations to ensure the success of our mission.