Director of Machine Learning

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.).

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