Senior Staff Machine Learning Engineer, Infrastructure

About This RoleAI processing…

Key Responsibilities

• 1
  Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning (ML) models for Airbnb product, business and operational use cases.
• 2
  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.
• 3
  Hands-on develop, productionize, and operate ML/AI models and pipelines at scale, including both batch and real-time use cases.
• 4
  Leverage third-party and in-house ML/AI 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.
• 5
  Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection.

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).
• Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (e.g. Hive).
• Industry experience building end-to-end ML/AI infrastructure and/or building and productionizing ML 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 building end-to-end AI/ML platforms and deploying production-grade AI/ML models.
• Familiarity with state-of-the-art LFMs such as Llama, Mixtral, CLIP, and the Qwen series.
• Hands-on experience developing RAG platform, leaderboards, chatbots, and agentic AI applications.
• Expertise in AI/ML governance, compliance, and regulatory frameworks.

Perks & Benefits