Senior Staff Machine Learning Engineer, Infrastructure

Airbnb Airbnb · Consumer · United States · Software Engineering

Senior Staff Machine Learning Engineer focused on building and scaling ML infrastructure, data foundations, and productionizing ML models at Airbnb. The role involves developing and operating ML/AI models and pipelines at scale, leveraging and improving ML/AI tools and infrastructure, and contributing to generative AI initiatives. Experience with RAG, agentic AI, and AI/ML governance is also required.

What you'd actually do

  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.

Skills

Required

  • Scala / Python / Java / C++ or equivalent
  • 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.

Nice to have

  • MS or PhD in relevant fields

What the JD emphasized

  • 12+ years of industry experience in applied ML/AI
  • Industry experience building end-to-end ML/AI infrastructure and/or building and productionizing ML models.
  • Experience building end-to-end AI/ML platforms and deploying production-grade AI/ML models.
  • Expertise in AI/ML governance, compliance, and regulatory frameworks.

Other signals

  • ML Infrastructure
  • Generative AI infrastructure
  • AI/ML data foundations
  • productionize and operate ML/AI models and pipelines at scale
  • end-to-end ML/AI infrastructure
  • deploying production-grade AI/ML models