Senior Machine Learning Engineer, Trust
$200,000–$235,000 year
HybridSan Francisco, California, United States
Job Summary
Frame and prototype ML and agentic solutions for trust and safety problems without established approaches, partnering with product managers and defense teams. Design, build, and productionize end-to-end Machine Learning pipelines for feature engineering, model training, evaluation, and deployment across batch and real-time use cases. Build and improve abuse behavior detection systems that generalize across defenses, while designing AI agents to automate trust decisions with orchestration and guardrails. Develop specialized models using LLMs and AI agents to accelerate solution building, and construct benchmarks and evaluation harnesses to measure decision quality objectively. Write, review, and ship clean, testable code to optimize scalability and reliability for large-scale structured and unstructured data. Validate solutions through experiments and holdholds with front line teams to quantify impact on business metrics.
Required Qualifications
- 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 building with agentic frameworks, orchestration, and evaluation
- Strong programming skills in Python (required)
- Solid understanding of Machine Learning best practices — e.g., 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 ML frameworks and tooling such as TensorFlow, PyTorch, or equivalent
- Experience with data engineering and building end-to-end ML pipelines, including both batch and real-time systems
- Experience designing evaluation methodology for ML or LLM systems — benchmarks, ground truth, offline/online metrics, calibration
- A Bachelor's, Master's, or PhD in CS/ML or a related field
Desired Qualifications
- familiarity with Scala, Java, or equivalent
- Comfort with ambiguity and a bias toward action: you can take a loosely defined problem, scope it, prototype quickly, and drive it to a measurable outcome
- Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high-volume data pipelines, efficient algorithms)
- Experience with test-driven development, incremental delivery, and deployment practices
- Experience with multimodal models (vision, document, or speech) is a plus
- Exposure to the Trust and Risk domain (e.g., fraud detection, anomaly detection, identity, account integrity) is a plus
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