Senior Machine Learning Engineer, Trust
New
A
AirbnbTrust and safety
This position is US - Remote Eligible.Full-TimeSenior
SalaryThe base pay range is $200,000 — $227,000 USD
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Job Details
- Experience
- 5-10 years of industry experience in applied Machine Learning; 1-2+ years of hands-on experience with LLMs and GenAI technologies.
- Required Skills
- PythonMachine LearningPyTorchTensorflowA/B testingGenerative AI
Requirements
- 5-10 years of industry experience in applied machine learning, with a track record of building and productionizing models at scale.
- 1-2+ years of hands-on experience with LLMs and GenAI technologies, including agentic frameworks, orchestration, and evaluation.
- Strong programming skills in Python; familiarity with Scala, Java, or equivalent.
- Knowledge of machine learning practices including training/serving skew minimization, A/B testing, feature engineering, and model selection.
- Experience with 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 end-to-end data engineering and machine learning pipelines, including batch and real-time systems.
- Experience designing evaluation methodologies for ML or LLM systems, including benchmarks, ground truth, offline/online metrics, and calibration.
- Exposure to architectural patterns for large-scale software applications, such as well-designed APIs, high-volume data pipelines, and efficient algorithms.
- Experience with test-driven development, incremental delivery, and deployment practices.
- Bachelor's, master's, or PhD in CS/ML or a related field.
- Experience with multimodal models or exposure to Trust and Risk topics such as fraud detection, anomaly detection, identity, or account integrity is a plus.
Responsibilities
- Frame and prototype ML and agentic solutions for ambiguous problems with product managers, data scientists, and front line defense teams.
- Design, build, and productionize end-to-end machine learning pipelines for batch and real-time use cases.
- Build and improve abuse behavior detection that generalizes across defenses.
- Design, launch, and iterate on AI agents that automate trust decisions, including orchestration, tool interfaces, and guardrails.
- Build benchmarks, evaluation harnesses, and instrumentation to measure and improve model and agent decision quality.
- Develop specialized models for trust and safety use cases and use LLMs and AI agents to accelerate model development.
- Write, review, and ship clean, testable code for models and scalable, reliable pipelines.
- Work with large-scale structured and unstructured data to improve machine learning models.
- Partner with front line defense teams to validate solutions through experiments and holdouts and quantify business and operational impact.
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