Staff ML Engineer
WFA Digital Insight
As demand for AI and machine learning specialists grows, companies like Docker are at the forefront of this shift. With over 20 million monthly users, Docker is a leader in developer tooling and is now looking for a Staff ML Engineer to join their team. This role is particularly interesting in the current remote job market, where skills in ML engineering, digital skills, and remote work are in high demand. According to recent statistics, the demand for ML engineers has increased by 25% in the past year, making this a lucrative career path. Before applying, candidates should be aware of the company's remote-first approach and the importance of collaboration and innovation in this role.
Job Description
About the Role
The Staff ML Engineer role at Docker is a hands-on builder position that requires shaping technical direction, shipping the first versions of intelligence capabilities, and growing the foundations of the team. As a founding engineer on the Intelligence Org, you will work directly with the team's first engineers and manager to figure out what to build, how to build it, and how it fits into the broader Docker platform. This role is critical in providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default.Docker's long-term vision is to become the runtime for trusted autonomy, and as AI agents redefine software development, the company is at the center of this shift. The Intelligence team builds intelligence-driven product capabilities that make software and agent execution on Docker safer, more effective, more trustworthy, and more efficient.
What You Will Do
- Design, train, evaluate, and ship ML systems that power governance and security capabilities
- 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
- 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
- Participate in a 24/7 on-call rotation for the Agentic Platform and carry genuine pager responsibility for the services you build and operate
- Collaborate with cross-functional teams to ensure seamless integration of ML capabilities
- Develop and maintain technical documentation for ML systems and infrastructure
- Stay up-to-date with industry trends and advancements in ML and AI
What We Are Looking For
- 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 in fraud, abuse, safety, security, or trust domains, where adversarial dynamics, imbalanced data, and high-stakes decisions are valuable
- Strong understanding of ML systems, including data pipelines, model serving, and evaluation methodologies
- Experience with modern AI tools and a sharp instinct for when frontier models can replace traditional ML
- Familiarity with LLM-based systems in production, including evaluation, prompt engineering, fine-tuning, retrieval, guardrails, and agent frameworks
Nice to Have
- Experience with the agent/MCP ecosystem
- Familiarity with Docker's products, including Docker Desktop, Docker Hub, and Docker Scout
- Experience with cloud-based infrastructure and containerization
- Strong understanding of software development principles and practices
- Experience with agile development methodologies and version control systems
Benefits and Perks
- Competitive salary and equity package
- Opportunity to work with a talented and experienced team
- Flexible working hours and remote work options
- Access to the latest technologies and tools
- Professional development opportunities, including training and conference attendance
- Comprehensive health insurance and retirement plan
- Generous paid time off and holiday policy
- Remote work stipend and home office setup support
How to Stand Out
- Develop a strong portfolio showcasing your ML engineering skills and experience with production systems.
- Highlight your understanding of ML systems, including data pipelines, model serving, and evaluation methodologies.
- Be prepared to discuss your experience with modern AI tools and frontier models, as well as your ability to make pragmatic build-vs-buy calls.
- Familiarize yourself with Docker's products and the company's vision for trusted autonomy.
- Showcase your ability to work collaboratively in a remote environment and your experience with agile development methodologies.
- Be prepared to discuss your experience with cloud-based infrastructure and containerization, as well as your understanding of software development principles and practices.
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