Senior Staff Machine Learning Engineer (Data & Eval)
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Aug 29, 2026. Airbnb scores B on the Alion truth index.
Join Airbnb as a Senior Staff Machine Learning Engineer, where you will set the technical direction and lead execution for ML evaluation and the data flywheel powering CSxAI products. You will work with large-scale structured and unstructured data, collaborate with cross-functional partners, and drive technical decisions and architecture for evaluation and data infrastructure. This role requires 10+ years of industry experience, a PhD in a related field, and a strong background in ML fundamentals and best practices.
Missions
- Set technical direction and lead execution for ML evaluation and the end-to-end data flywheel powering CSxAI products.
- Define how to measure quality, turn feedback into learning signals, and continuously improve models and products.
- Partner closely with product, engineering, design, and operations to build evaluation systems that are trusted, scalable, and actionable.
Profil recherché
- Technical Proficiency:- Industry Experience: 10+ years building, testing, and shipping ML/AI systems end-to-end; including 2+ years of experience with GenAI/LLM systems in production
- Deep expertise in evaluation methodology (offline/online alignment, metric design, human-in-the-loop evaluation, A/B testing, power analysis, regression testing)
- Educational Background: PhD in Computer Science, Mathematics, Statistics, or related technical field (or equivalent practical experience)
- Leadership Experience: 5+ years leading large, ambiguous technical initiatives as a senior IC, influencing roadmap and engineering/science direction across teams
- Solid ML fundamentals and best practices (model selection, training/serving, monitoring, reliability, and model lifecycle management)
- Hands-on experience with GenAI systems, including orchestration, retrieval, tool calling, memory, etc
- Experience building data pipelines and quality systems (labeling workflows, dataset curation, versioning, monitoring, and governance)
- Continuous Learner: Strong curiosity and ability to absorb new techniques (e.g., judge models, preference optimization, synthetic data generation) and apply them pragmatically
- Infrastructure & Quality at Scale: Experience building robust evaluation platforms for agent behavior validation, safety/guardrails, and continuous improvement
- Agile Practice for Applied AI: Proven ability to take evaluation and data flywheel work from incubation to production, iterating quickly while maintaining scientific rigor
- Customer Support Systems: Experience applying ML/AI to customer support workflows (e.g., agent assist, classification/routing, resolution recommendation, QA)