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Senior Machine Learning Engineer, Trust

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Senior Machine Learning Engineer, Trust in USA

  • San Francisco, CA

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.

Salary: $200k - $235k/yr

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