Staff ML Engineer

Docker Inc.

Job Overview

Location

Palo Alto, CA

Job Type

Full-time

Full Job Description

📋 Description

• Design, train, evaluate, and ship machine learning systems to power governance and security capabilities including prompt injection detection, behavioral anomaly detection, trust scoring, and policy recommendations.
• Build and maintain supporting infrastructure for ML systems including data pipelines, feature stores, model serving, evaluation harnesses, and feedback loops to enable rapid iteration.
• Make pragmatic decisions on build-vs-buy strategies, leveraging frontier models, off-the-shelf tooling, and managed services while investing in custom systems where they create durable competitive advantage.
• Set and own the technical direction for the Intelligence team’s ML work, including architecture, evaluation methodology, model lifecycle management, and the bar for production shipping.
• Serve as a founding engineer on the Intelligence Org, collaborating closely with the first engineers and manager to define what to build, how to build it, and how it integrates with Docker’s broader platform.
• Ship the first versions of intelligence capabilities into customer hands, establishing foundational systems that will scale as the team grows.
• Participate in a 24/7 on-call rotation for the Agentic Platform with genuine pager responsibility for services you build and operate.
• Help recruit, mentor, and shape the growth of the Intelligence team as it expands.
• Work fluently with LLM-based systems in production, including evaluation, prompt engineering, fine-tuning, retrieval, guardrails, and agent frameworks.
• Apply familiarity with the agent and MCP ecosystem to inform system design and implementation decisions.
• Operate in an early-stage environment where the roadmap is evolving in real time, requiring crisp decision-making with incomplete information.
• Collaborate effectively across teams, communicate clearly in writing, and bring others along with a low-ego, inclusive approach.
• Leverage deep applied ML/AI expertise to solve problems in fraud, abuse, safety, security, or trust domains involving adversarial dynamics, imbalanced data, and high-stakes decisions.
• Apply 8+ years of hands-on software engineering experience in backend, infrastructure, or platform engineering to build scalable, reliable systems.
• Own end-to-end delivery of customer-facing ML products, including data ingestion, model training, deployment, monitoring, and iteration.
• Use modern AI tools daily and demonstrate strong intuition for when to use frontier models, when to use traditional ML, and when to combine both approaches.

🎯 Requirements

• 5+ years of deep applied ML/AI expertise with a track record of shipping production systems
• 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
• Experience building and owning systems around ML models (data pipelines, serving, evaluation, monitoring) and shipping customer-facing products end to end
• Experience with LLM-based systems in production (evaluation, prompt engineering, fine-tuning, retrieval, guardrails, agent frameworks)
• Familiarity with the agent / MCP ecosystem

🏖️ Benefits

• Freedom & flexibility; fit your work around your life
• Designated quarterly Whaleness Days plus end of year Whaleness break
• Home office setup; we want you comfortable while you work
• 16 weeks of paid Parental leave (after 6 months of employment)
• Technology stipend equivalent to $100 USD net/month
• PTO plan that encourages you to take time to do the things you enjoy

Skills & Technologies

Docker

Data Science

Senior

Remote

Degree Required

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Docker Inc.

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About Docker Inc.

Docker Inc. provides an open platform for developing, shipping, and running applications inside lightweight containers. Its tools package software and dependencies into portable units that run consistently across environments, accelerating DevOps workflows and cloud-native development. The company offers Docker Desktop, Hub, and subscription services that integrate with CI/CD pipelines and orchestration platforms, enabling teams to build, share, and deploy microservices at scale while maintaining security and governance policies.

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