ML Engineer

About the Role

The Intelligence team at Docker is responsible for building intelligence-driven product capabilities that make software and agent execution safer, more effective, and more trustworthy. As an ML Engineer, you will play a critical role in shaping the technical direction of the team and driving the development of AI-powered systems that power governance and security capabilities.

Docker's long-term vision is to become the runtime for trusted autonomy, and the Intelligence team is at the center of this effort. As an ML Engineer, you will have the opportunity to work on cutting-edge projects that combine AI, software development, and cybersecurity.

What You Will Do

• 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, using frontier models, off-the-shelf tooling, and managed services to move quickly, and investing in custom systems where they create durable advantage.
• Set technical direction for the team's ML work, owning the architecture, evaluation methodology, model lifecycle, and the bar for shipping.
• Help recruit, mentor, and shape the team as it grows, providing guidance and support to junior engineers.
• Collaborate with cross-functional teams, including product, engineering, and design, to ensure that ML systems meet business requirements and are integrated into the larger product ecosystem.
• Develop and maintain technical documentation, including architecture diagrams, design documents, and technical guides.
• Participate in a 24/7 on-call rotation for the Agentic Platform, carrying genuine pager responsibility for the services you build and operate.

What We Are Looking For

• 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 are 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.
• Experience building and owning the systems around ML models, including data pipelines, serving, evaluation, monitoring, and more.
• Fluency in modern AI tools and 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, including evaluation, prompt engineering, fine-tuning, retrieval, guardrails, and agent frameworks.
• Familiarity with the agent/MCP ecosystem and the ability to work in a fast-paced, agile environment.

Nice to Have

• Experience with containerization technologies, such as Docker, and a strong understanding of software development principles.
• Knowledge of cybersecurity principles and practices, including threat modeling, vulnerability assessment, and penetration testing.
• Experience with cloud-based infrastructure, including AWS, Azure, or Google Cloud, and a strong understanding of cloud security best practices.

Benefits and Perks

• Competitive salary and equity package, with a comprehensive benefits package, including health, dental, and vision insurance.
• Flexible work arrangements, including remote work options, and a generous PTO policy.
• Opportunities for professional growth and development, including training, mentorship, and conference attendance.
• Access to cutting-edge technologies and tools, including AI and machine learning frameworks, and a budget for professional development and education.
• A collaborative and dynamic work environment, with a team of experienced engineers and researchers, and a strong focus on innovation and experimentation.
• A comprehensive wellness program, including mental health support, fitness classes, and on-site wellness initiatives.

• Develop a strong portfolio of ML projects, including examples of applied ML and AI in software development, and be prepared to discuss your design decisions and technical trade-offs.
• Showcase your expertise in modern AI tools and frameworks, including LLM-based systems, and demonstrate your ability to work with large datasets and complex systems.
• Highlight your experience with containerization technologies, such as Docker, and your understanding of software development principles, including version control, testing, and deployment.
• Be prepared to discuss your approach to testing and validation, including your experience with evaluation methodologies and metrics, and your ability to iterate quickly and make data-driven decisions.
• Research the company culture and values, and be prepared to discuss how your skills and experience align with Docker's mission and vision, and how you can contribute to the company's growth and success.
• Prepare to back your claims with specific examples from your past experience, and be ready to answer behavioral questions that assess your problem-solving skills, communication skills, and ability to work in a team.
• Don't be afraid to ask questions during the interview process, and be prepared to discuss your salary expectations and requirements, including any specific benefits or perks you are looking for.