2 months ago
Base Salary
$200k - $235k/yr
Responsibilities
- Frame and prototype machine learning and agentic solutions for ambiguous trust and safety problems.
- Design, build, and productionize end-to-end machine learning pipelines covering feature engineering, training, evaluation, and deployment.
- Build and improve abuse behavior detection across multiple defenses.
- Design, launch, and iterate on AI agents that automate trust decisions, including orchestration, tool interfaces, and guardrails.
- Create benchmarks, evaluation harnesses, and instrumentation to measure model and agent decision quality.
- Develop specialized trust and safety models and use LLMs and AI agents to accelerate model development.
- Write, review, and ship clean, testable code while improving scalability and reliability.
- Work with large-scale structured and unstructured data to improve models.
- Partner with frontline defense teams to validate solutions through experiments and holdouts and quantify business and operational impact.
- Participate in code reviews, design discussions, and cross-team collaboration.
Requirements
- 5–10 years of industry experience in applied machine learning and a track record of building and productionizing models at scale.
- 1–2+ years of hands-on experience with LLMs and generative AI, including agentic frameworks, orchestration, and evaluation.
- Strong Python programming skills and familiarity with Scala, Java, or equivalent.
- Knowledge of machine learning practices including training/serving skew minimization, A/B testing, feature engineering, model selection, and algorithms such as gradient boosted trees, neural networks, transformers, and deep learning.
- Experience with TensorFlow, PyTorch, or equivalent machine learning frameworks and tooling.
- Experience building data engineering systems and end-to-end machine learning pipelines for batch and real-time use cases.
- Experience designing evaluation methodologies for machine learning or LLM systems, including benchmarks, ground truth, offline and online metrics, and calibration.
- Exposure to large-scale software architecture, well-designed APIs, high-volume data pipelines, and efficient algorithms.
- Experience with test-driven development, incremental delivery, and deployment practices.
- Experience with multimodal models is preferred.
- Exposure to trust and risk domains such as fraud detection, anomaly detection, identity, or account integrity is preferred.
- Bachelor’s, master’s, or PhD in computer science, machine learning, or a related field.
Benefits
- US remote eligible, with occasional office or offsite work as agreed with the manager.
- May be eligible for bonus, equity, benefits, and Employee Travel Credits.
Categories
About Airbnb
Airbnb operates a global online marketplace that connects hosts offering homes, rooms, and unique properties with travelers seeking short-term stays and experiences. The company earns revenue primarily through service fees on bookings; founded in 2007 and headquartered in San Francisco, it is a public company on NASDAQ (ABNB) with over 5 million hosts and guest arrivals recorded in nearly every country.