ML Engineer

Docker is a globally distributed, remote-first team building tools trusted by over 20 million monthly users and powering over 20 billion container image pulls. As AI agents redefine software development, Docker provides the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. The Intelligence team builds intelligence-driven capabilities to enhance safety, effectiveness, and efficiency across the Docker platform.

About The Role

We're hiring a Machine Learning Engineer as one of the founding engineers on the Intelligence team. You'll work directly with the first engineers and manager to define what to build, how to build it, and how it integrates into the broader Docker platform. This is a hands-on builder role with staff-level scope: you'll shape technical direction, ship initial intelligence capabilities to customers, and establish foundational elements like data, evaluation, and infrastructure that the team will scale upon.

Responsibilities

• Design, train, evaluate, and ship ML systems that power governance and security capabilities, including prompt injection detection, behavioral anomaly detection, trust scoring, and policy recommendations.
• Build supporting infrastructure: data pipelines, feature stores, model serving, evaluation harnesses, and feedback loops to enable fast iteration.
• Make pragmatic build-vs-buy decisions, leveraging frontier models, off-the-shelf tooling, and managed services while investing in custom systems where they provide durable advantage.
• Set technical direction for the team's ML work, owning architecture, evaluation methodology, model lifecycle, and shipping standards.
• Help recruit, mentor, and shape the team as it grows.
• Participate in a 24/7 on-call rotation for the Agentic Platform, carrying pager responsibility for the services you build and operate.

Qualifications

• 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 is valuable.
• 4+ years of professional, hands-on 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, including data pipelines, serving, evaluation, and monitoring, with end-to-end product shipping.
• Fluency with modern AI tools and a sharp instinct for when frontier models can replace traditional ML, when they can't, and when to combine both.
• Experience with LLM-based systems in production, including evaluation, prompt engineering, fine-tuning, retrieval, guardrails, and agent frameworks.
• Familiarity with the agent/MCP ecosystem.
• Ability to thrive in early-stage efforts where the roadmap is evolving, making crisp decisions with incomplete information.
• Collaborative and low-ego, with strong cross-team communication and clarity in writing.

Docker considers visa sponsorship on a case-by-case basis based on business needs.

Pay

United States: $138,500 – $225,500 + equity

Benefits

• Freedom and flexibility to fit work around your life.
• Designated quarterly Whaleness Days plus an end-of-year Whaleness break.
• Home office setup stipend for comfort while working.
• 16 weeks of paid parental leave (after 6 months of employment).
• Technology stipend equivalent to $100 USD net/month.
• PTO plan encouraging personal time.
• Training stipend for conferences, courses, and classes.
• Equity in a growing start-up.
• Docker swag.
• Medical benefits, retirement, and holidays vary by country.

Remote-first culture, with offices in Seattle and Paris.