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ML Engineer

139k – 226kPalo Alto, CASeattle, WAML EngineeringRemote5+ YOE
Summary

Founding ML Engineer building production ML systems for governance, security, and agentic platform capabilities at Docker. Requires 5+ years applied ML experience shipping systems and 4+ years backend/infra engineering.

About the role

Responsibilities

  • Design, train, evaluate, and ship ML systems that power governance and security capabilities, starting with problems like prompt injection detection, behavioral anomaly detection, trust scoring, and policy recommendations.
  • Build the supporting infrastructure: data pipelines, feature stores, model serving, evaluation harnesses, and the feedback loops that make iteration fast.
  • Make pragmatic build-vs-buy calls. Use frontier models, off-the-shelf tooling, and managed services to move quickly; invest in custom systems where they create durable advantage.
  • Set technical direction for the team's ML work. Own the architecture, evaluation methodology, model lifecycle, and the bar for shipping.
  • Help recruit, mentor, and shape the team as it grows.
  • Participate in a 24/7 on-call rotation for the Agentic Platform.

Requirements

  • 5+ years of deep applied ML/AI expertise with a track record of shipping production systems.
  • Experience in fraud, abuse, safety, security, or trust domains, where adversarial dynamics, imbalanced data, and high-stakes decisions is valuable.
  • 4+ years of professional, hands-on, full-time software engineering experience in backend, infrastructure, or platform engineering.
  • Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
  • You've built and owned the systems around ML models, i.e. data pipelines, serving, evaluation, monitoring etc. and have shipped customer-facing products end to end.
  • You use modern AI tools fluently in your day-to-day work and have a sharp instinct for when frontier models can replace traditional ML, when they can't, and when to combine the two.
  • Experience with LLM-based systems in production - evaluation, prompt engineering, fine-tuning, retrieval, guardrails, agent frameworks.
  • Familiarity with the agent / MCP ecosystem.
  • Energized by an early-stage effort where the roadmap is being written as the work happens, and you make crisp decisions with incomplete information.
  • Collaborative and low-ego. You work well across teams, write clearly, and bring others along.
Skills
Machine LearningLLMsPrompt EngineeringFine-tuningRetrievalGuardrailsAgent FrameworksData PipelinesModel ServingEvaluationBackend EngineeringInfrastructure
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