Staff ML Engineer
Founding Staff ML Engineer building production ML systems for governance, security, and agentic platform capabilities at Docker. Owns architecture, data pipelines, evaluation, and model lifecycle while mentoring the growing team.
About the job
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.
- 8+ 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 Learning, LLMs, Prompt Engineering, Fine-Tuning, Retrieval, Guardrails, Agent Frameworks, Data Pipelines, Model Serving, Evaluation, Backend Engineering, Infrastructure
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