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 for batch and real-time use cases, including feature engineering, model 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 quality guardrails.
• Build benchmarks, evaluation harnesses, and instrumentation to measure agentic and model 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 model pipelines and scalability.
• Work with large-scale structured and unstructured data to continuously improve machine learning models.
• Partner with front-line 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, with 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.
• Understanding of machine learning best practices, including training/serving skew minimization, A/B testing, feature engineering, and model selection.
• Knowledge of gradient-boosted trees, neural networks, transformers, and deep learning.
• Experience with TensorFlow, PyTorch, or equivalent machine learning frameworks.
• Experience with data engineering and end-to-end machine learning pipelines, including batch and real-time systems.
• Experience designing evaluation methodologies for machine learning or LLM systems, including benchmarks, ground truth, offline and online metrics, and calibration.
• Exposure to architectures for large-scale software applications, 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 computer science, machine learning, or a related field.

Nice-to-haves

• Experience with multimodal models, including vision, document, or speech models.
• Exposure to trust and risk domains such as fraud detection, anomaly detection, identity, or account integrity.

Compensation and Benefits

• Base pay range: $200,000–$235,000 USD.
• May also be eligible for bonus, equity, benefits, and Employee Travel Credits.