Director of Machine Learning

Virtualitics

Director leading machine learning engineering for Virtualitics, a defense tech company building AI-native military readiness intelligence. Architecting production AI systems and guiding engineering teams.

Posted 8/10/2026full-timeDistrict of Columbia • 🇺🇸 United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in leading Machine Learning Engineering teams, architecting scalable applications, and managing the complete data lifecycle. Proficient in deploying production-grade AI systems and optimizing performance while collaborating across teams to align engineering deliverables with business objectives.

Highest-signal resume keywords

Machine Learning Engineering LeadershipProduction Deployment ExperiencePython ProficiencyAI System DesignData Lifecycle Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringAI/Agentic Systems DeploymentTraining and Inference Pipeline DesignFull-Stack Software DevelopmentDevSecOpsData ProcessingPerformance OptimizationMachine Learning FrameworksBig Data TechnologiesSecurity Clearance Management

Soft Skills

MentoringTechnical CommunicationCollaboration

Tools & Technologies

DockerKubernetesGitPython Data StackPandasNumPyScikit-learnPyTorchMatplotlibSpark

Certifications & Qualifications

U.S. Secret Security ClearanceTS/SCI Clearance (Willingness to Upgrade)

Industry Keywords

Machine LearningAI SystemsData LifecycleProduction EnvironmentsEngineering Excellence

Tech Stack

Tools & technologies

DockerKubernetesNumpyPandasPythonPyTorchScikit-LearnSparkTypeScript

About the role

Key responsibilities & impact

Lead the Machine Learning Engineering team
Architect robust, scalable applications using sound engineering principles
Manage the complete data lifecycle from acquisition through model inference and postprocessing
Improve runtime performance and optimize data access patterns for responsive applications
Collaborate with Product, Customer Success, DevOps, and QA to align engineering deliverables with customer commitments
Address key issues across Delivery and Platform teams and identify pain points for roadmap influence
Set the technical hiring bar and mentor engineers
Communicate technical progress, risks, and ROI, linking AI team output to revenue and mission impact

Requirements

What you’ll need

Must hold at least a U.S. Secret security clearance
Must be willing to upgrade to a TS/SCI clearance if needed
Must be willing to travel to customer locations as needed
Degree in Computer Science or a related field
8+ years of software engineering experience
Strong software engineering and production deployment background
Proven track record deploying software into production environments
Experience shipping production-grade AI/agentic systems
Experience offloading AI compute to GPUs
Comfortable designing training and inference pipelines
Understanding of full-stack software development, DevSecOps, and AI systems holistically
Familiarity with Docker, Kubernetes, and Git
Proficiency in Python and the Python Data Stack, including pandas, NumPy, scikit-learn, PyTorch, and Matplotlib
Experience working with structured and unstructured data from diverse sources
Preferred: experience building and cultivating a high-functioning Machine Learning Engineering team
Preferred: experience contributing to engineering excellence and top-tier engineering experience
Preferred: experience with Spark, Databricks, Snowflake, or similar big data technologies

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account Virtualitics Website LinkedIn All Job Openings 51 - 200 employees Founded 2016 🎖️ Defense 🤖 Artificial Intelligence ☁️ SaaS 🔥 Funding within the last year 💰 $15M Debt Financing - Virtualitics on 2025-09 Defense
Artificial Intelligence
SaaS Virtualitics is an AI-native software company that provides readiness and decision-support solutions purpose-built for defense organizations. Their flagship product, Virtualitics Iris, combines generative AI and specialized readiness AI agents to analyze fragmented readiness data and accelerate decisions across maintenance, materiel, personnel, supply chain, and mission modernization domains. Deployed across U. S. military services and grounded in research origins tied to Caltech and NASA JPL, Virtualitics emphasizes operational, mission-aware AI delivered as a platform to reduce analysis time, predict failures, and produce actionable courses of action for defense customers. Director of Machine Learning Job not on LinkedIn 🔥 0 minutes ago 🏢🏡 null – Hybrid ⏰ Full Time 🔴 Lead 🤖 Machine Learning Engineer Docker Kubernetes Numpy Pandas Python PyTorch Scikit-Learn Spark TypeScript Apply Now Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Lead the Machine Learning Engineering team
Architect robust, scalable applications using sound engineering principles
Manage the complete data lifecycle from acquisition through model inference and postprocessing
Improve runtime performance and optimize data access patterns for responsive applications
Collaborate with Product, Customer Success, DevOps, and QA to align engineering deliverables with customer commitments
Address key issues across Delivery and Platform teams and identify pain points for roadmap influence
Set the technical hiring bar and mentor engineers
Communicate technical progress, risks, and ROI, linking AI team output to revenue and mission impact 🎯 Requirements
Must hold at least a U.S. Secret security clearance
Must be willing to upgrade to a TS/SCI clearance if needed
Must be willing to travel to customer locations as needed
Degree in Computer Science or a related field
8+ years of software engineering experience
Strong software engineering and production deployment background
Proven track record deploying software into production environments
Experience shipping production-grade AI/agentic systems
Experience offloading AI compute to GPUs
Comfortable designing training and inference pipelines
Understanding of full-stack software development, DevSecOps, and AI systems holistically
Familiarity with Docker, Kubernetes, and Git
Proficiency in Python and the Python Data Stack, including pandas, NumPy, scikit-learn, PyTorch, and Matplotlib
Experience working with structured and unstructured data from diverse sources
Preferred: experience building and cultivating a high-functioning Machine Learning Engineering team
Preferred: experience contributing to engineering excellence and top-tier engineering experience
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