Director of Machine Learning
Summary
Lead a team building AI-native readiness intelligence for defense, focusing on agentic workflows, composable AI agents, and scalable ML systems for mission-critical data.
Director of Machine Learning Engineering (Secret Cleared, DMV)
About Virtualitics:
Virtualitics is a fast-growing, Caltech-born defense tech company (~120 people) dedicated to reversing
the decline in military readiness. Virtualitics builds AI-native readiness intelligence for the US and allied
nations’ defense sector. Virtualitics Iris transforms how defense organizations act on maintenance,
logistics, personnel, and global force management data. Our team is actively refining agentic workflows,
composable AI agents, and generative interfaces to replace static dashboards with dynamic,
conversational intelligence.
What You Will Do:
- Lead the Machine Learning Engineering team.
- Provide guidance on how to architect robust, scalable applications using sound engineering
principles, managing the complete data lifecycle from acquisition to model inference and
postprocessing. - Tackle runtime performance and optimize data access patterns for highly responsive applications.
- Collaborate across the delivery team (e.g. Product, Customer Success, DevOps, and QA) to align
engineering deliverables with strategic customer commitments. - Tackle key issues across Delivery and Platform teams and flag pain points to help influence the
roadmap. - Set the technical hiring bar and mentor engineers, ensuring teams are well-staffed and capable.
- Clearly communicate technical progress, risks, and ROI, directly linking AI team output to
revenue, mission impact both up and down as well as internally and externally.
Core Requirements:
- Clearance & Location: Must hold at least a U.S. Secret security clearance and be willing to
upgrade to a TS/SCI if needed. Must be willing to travel to customer locations as needed. - Engineering Fundamentals: A degree in Computer Science or related field and 8+ years of
software engineering experience. We target candidates with a strong background in software
engineering and production deployment, rather than strictly research-oriented backgrounds. - AI & Systems: A proven track record of deploying software into production environments. Has
shipped production-grade AI / agentic systems. - GPU Fluency: Has experience with offloading compute for AI systems to GPUs and is
comfortable with designing training and inference pipelines. - Full Stack & DevSecOps: Understands full stack software development, DevSecOps, and AI
systems holistically. Familiarity with Docker, Kubernetes, and Git. - Data Ecosystem: Proficiency in Python with a solid understanding of the Python Data Stack
(pandas, NumPy, scikit-learn, PyTorch, Matplotlib, etc.). Experience working with a wide variety of
data (both structured and unstructured) from different sources. - Culture & Values: Embody Virtualitics core values by bringing a positive attitude, fostering a
highly collaborative environment, and always being ready to "lean in" to tackle complex
challenges alongside the team.
Preferred Qualifications (Pluses):
- Has built and cultivated a high functioning Machine Learning Engineering team before.
- Has contributed to building engineering excellence and has top-tier engineering experience.
- Experience with big data technologies and frameworks (Spark, Databricks, Snowflake, etc.).