Airbnb9 days ago
Senior Machine Learning Engineer, Trust
Salary not specified
MARKET
15,900 ₽median for this role
Data Scientist · 115 jobs with disclosed pay
5,000half of the offers: 12,796–20,50043,793
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San Francisco
Responsibilities
- 01Frame and prototype ML and agentic solutions for problems that do not yet have an established approach
- 02Design, build, and productionize end-to-end Machine Learning pipelines including feature engineering, model training, evaluation, and deployment
- 03Build and improve abuse behavior detection that generalizes across defenses
- 04Design, launch, and iterate on AI agents that automate trust decisions including orchestration, tool interfaces, and guardrails
- 05Build benchmarks, evaluation harnesses, and instrumentation to measure agentic and model decision quality
- 06Develop specialized models for trust and safety use cases using LLMs and AI agents
- 07Write, review, and ship clean, testable code for models and pipelines
- 08Work with large-scale structured and unstructured data to improve ML models
- 09Partner with front line defense teams to validate solutions through experiments and quantify impact
Requirements
- 015-10 years of industry experience in applied Machine Learning with track record of building and productionizing models at scale
- 021-2+ years of hands-on experience with LLMs and GenAI technologies including building with agentic frameworks, orchestration, and evaluation
- 03Strong programming skills in Python
- 04Familiarity with Scala, Java, or equivalent
- 05Solid understanding of Machine Learning best practices including training/serving skew minimization, A/B testing, feature engineering, model selection
- 06Knowledge of algorithms such as gradient boosted trees, neural networks, transformers, and deep learning
- 07Experience with ML frameworks and tooling such as TensorFlow, PyTorch, or equivalent
- 08Experience with data engineering and building end-to-end ML pipelines including both batch and real-time systems
- 09Experience designing evaluation methodology for ML or LLM systems including benchmarks, ground truth, offline/online metrics, calibration
- 10Comfort with ambiguity and bias toward action
- 11Exposure to architectural patterns of large, high-scale software applications
- 12Experience with test-driven development, incremental delivery, and deployment practices
- 13Bachelor's, Master's, or PhD in CS/ML or related field