Senior Software Engineer, Assistant Engineering

Responsibilities

• Design and productionize scalable data systems that support AI evaluation, metric computation, observability, and feedback loops for agentic AI products.
• Build data models, schemas, and processing pipelines for agentic AI interactions, supporting reliable logging, retrieval, metric computation, and long-term evaluation dataset management.
• Work closely with Core Modeling engineers to understand pain points in the LLM evaluation process, and develop LLM-as-a-judge solutions and data pipelines to address metric-related challenges in a scalable and efficient way.
• Collaborate with machine learning infrastructure engineering teams to evolve how we build and test evaluation framework for Airbnb Conversational AI products.
• Lead all phases of software development including architecture design, implementation and testing.
• Work collaboratively with cross-functional partners including product managers, operations and data scientists, identify opportunities for business impact, understand and prioritize requirements for machine learning systems and data pipelines, drive engineering decisions and quantify impact.
• Foster a culture of engineering excellence by supporting teammates in writing high-quality code, ensuring operational reliability, and sharing knowledge across the team.

Requirements

• 5+ years of industry experience as a software engineer, backend engineer, platform engineer, or data-focused software engineer building production systems.
• BS, MS, or PhD in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• Strong programming skills in Python, with experience building production-quality software, libraries, frameworks, or data processing systems.
• Experience designing and operating scalable data pipelines using Airflow or similar orchestration frameworks.
• Strong understanding of data modeling, schema design, data quality, partitioning, indexing, storage formats, and tradeoffs for large-scale analytical and operational data systems.
• Experience building data layers, evaluation systems, feedback loops, experimentation platforms, observability tools, or AI/ML platform capabilities.
• Ability to analyze complex datasets, identify data quality issues, debug inconsistencies, and translate findings into actionable engineering or product decisions.
• Strong system design skills, including experience building reliable, extensible, maintainable systems with clear APIs, testing strategies, and operational ownership.
• Solid understanding of data structures and algorithms, with the ability to make practical engineering tradeoffs for performance, scalability, and maintainability.
• Familiarity with AI/ML system concepts such as model evaluation, offline evaluation, online monitoring, model quality metrics, human-in-the-loop workflows, experimentation, or model deployment.
• Proven ability to work cross-functionally with modeling engineers, product managers, data scientists, infrastructure teams, and operations partners to deliver end-to-end solutions.
• Excellent communication with the ability to drive alignment, set technical direction, and raise engineering quality across a team.

Nice-to-Haves

• Experience with Spark, Flink, Kafka, Trino/Presto, Hive, Iceberg, or similar big data technologies.