ML Engineer

Docker, Inc

ML Engineer developing intelligence-driven product capabilities for Docker's platform. Collaborating with founding engineers to shape technical direction and build ML systems that enhance security and governance.

Posted 6/15/2026full-timeRemote • California • 🇺🇸 United StatesMid-LevelSenior💰 $138,500 - $225,500 per yearWebsite

About the role

Key responsibilities & impact

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.
This role may require participation in a 24/7 on-call rotation for the Agentic Platform; carry genuine pager responsibility for the services you build and operate

Requirements

What you’ll need

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.
You're 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.

Benefits

Comp & perks

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
Training stipend for conferences, courses and classes
Equity; we are a growing start-up and want all employees to have a share in the success of the company
Docker Swag
Medical benefits, retirement and holidays vary by country
Remote-first culture, with offices in Seattle and Paris

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningartificial intelligencedata pipelinesmodel servingevaluation methodologyprompt engineeringfine-tuningmonitoringbackend engineeringinfrastructure engineering

Soft Skills

collaborationmentoringdecision makingcommunicationteam leadershipadaptabilityproblem solvinglow-egoorganizationcritical thinking

Certifications

Bachelor's degree in Computer ScienceBachelor's degree in Engineeringequivalent practical experience