Senior Machine Learning Engineer, Trust

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.