Senior Staff Machine Learning Engineer, Infrastructure

📋 Description

• Engage with extensive structured and unstructured datasets, innovating and consistently enhancing advanced Machine Learning (ML) models for various Airbnb product, business, and operational applications.

• Collaborate effectively with cross-functional teams, including software engineers, product managers, operations, and data scientists, to identify business impact opportunities, comprehend, refine, and prioritize machine learning model requirements, influence engineering decisions, and assess impact.

• Actively develop, deploy, and manage ML/AI models and pipelines at scale, accommodating both batch processing and real-time scenarios.

• Utilize both third-party and proprietary ML/AI tools and infrastructure to create reusable, highly distinctive, and high-performing Machine Learning systems that facilitate rapid model development, low-latency serving, and simplified model quality maintenance.

• Illustrative projects encompass: feature platform, model interpretability, hyperparameter optimization, and concept drift detection.

⛳️ Requirements

• A minimum of 12 years of industry experience in applied ML/AI, including an MS or PhD in relevant disciplines.

• Proficient programming skills in languages such as Scala, Python, Java, C++, or similar, along with strong data engineering capabilities.

• Comprehensive knowledge of ML/AI best practices (e.g., minimizing training/serving skew, A/B testing, 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).

• Familiarity with three or more of the following technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse solutions (e.g., Hive).

• Proven experience in constructing end-to-end ML/AI infrastructure and/or developing and deploying ML models in production.

• Knowledge of architectural patterns in large-scale software applications (e.g., well-structured APIs, high-volume data pipelines, efficient algorithms, models).

• Experience in test-driven development, with a solid understanding of A/B testing, incremental delivery, and deployment methodologies.

• Background in building comprehensive AI/ML platforms and deploying production-grade AI/ML models.

• Acquainted with state-of-the-art LFMs such as Llama, Mixtral, CLIP, and the Qwen series.

• Practical experience in developing RAG platforms, leaderboards, chatbots, and agentic AI applications.

• Specialized knowledge in AI/ML governance, compliance, and regulatory frameworks.

🏝️ Benefits

• Bonuses

• Equity

• Employee Travel Credits