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:

Responsibilities

• 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:

• 8+ 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.
• Experience with developing machine learning models at scale from inception to business impact
• Proven ability to tailor your solutions to business problems in a cross functional team (eg. design, product, data science, operations, and research)
• Excellent communication skills, both written and verbal
• Steadfast focus on creating impactful change