ML Software Engineer Visa Sponsorship Jobs in Virginia
Virginia is one of the strongest states for ML software engineer visa sponsorship, anchored by a dense corridor of tech employers in Northern Virginia, including Amazon Web Services, Booz Allen Hamilton, Leidos, and Capital One. The state's proximity to federal agencies and defense contractors creates consistent demand for ML engineers with security-eligible backgrounds.
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The Data Scientist, AI Metrics & Portal is a technical role responsible for owning the full lifecycle of AI Program metrics, including defining, architecting, implementing, operationalizing, and continuously improving a standardized AI metrics capability. This role combines data science, analytics engineering, artificial intelligence, and software development to: (1) Establish AI Program metrics—from conceptual definition through technical implementation and ongoing optimization, and (2) Design, build, and operate a modern, lightweight AI Metrics Hub, leveraging Claude Code and other tech stack tools to rapidly develop and maintain an extensible analytics platform. The Data Scientist will define and operationalize standardized AI metrics, architect the supporting data and application layers, implement dynamic visualization and AI-driven querying capabilities, and ensure continuous evolution of the platform to meet business needs. The role will orchestrate metrics design, platform engineering, and Agile delivery practices to:
- Define, standardize, and govern AI metrics across adoption, utilization, performance, value, cost, risk, and other categories.
- Architect scalable data models and metrics frameworks to ensure consistency and reuse.
- Implement and operationalize metrics pipelines, logic, and computation layers.
- Design and build an analytics platform with AI metrics catalog, standard/pre-configured AI dashboards, and self-service AI dashboards and exploration.
- Implement AI-powered natural language querying and discovery capabilities.
- Maintain and evolve metrics definitions, lineage, and supporting documentation.
- Deliver iteratively using Agile and SAFe methodologies.
- Enable continuous improvement and future integration with enterprise platforms (e.g., Databricks, Collibra).
This role requires a balance of hands-on implementation, architecture ownership, and delivery leadership, with accountability for the end-to-end lifecycle of AI metrics and insights capabilities.
Key Responsibilities
1. AI Metrics Lifecycle Ownership (Define → Architect → Implement → Operate → Evolve)
- Own the full lifecycle of AI metrics, including:
- Definition and standardization
- Architectural design
- Technical implementation
- Operational monitoring
- Continuous improvement
- Define and maintain a comprehensive AI metrics framework, including:
- Adoption, utilization, engagement
- Business value and ROI
- Performance and quality
- Risk, compliance, and cost
- Translate business questions into well-defined, implementable metrics and models
2. Metrics Architecture & Standardization
- Architect scalable, reusable metric models, including:
- KPI definitions and calculation logic
- Dimensional structures and aggregation strategies
- Establish and enforce standards for consistency, governance, and reuse
- Ensure metrics are designed for extensibility and enterprise integration
3. Metrics Implementation & Data Engineering
- Design and implement metrics computation pipelines and transformations
- Develop and maintain SQL and Python logic for KPI calculation
- Integrate and normalize data from multiple sources (logs, APIs, databases, surveys, risk reviews, and more)
- Ensure data accuracy, consistency, and performance optimization
- Implement data quality validation and monitoring processes
4. AI Metrics Portal Development
- Architect, build, and maintain the AI Metrics Hub application
- Develop platform components, including:
- Metrics registry (definitions, metadata, ownership)
- Dynamic dashboard and visualization engine
- Config-driven metric execution layer
- Leverage AI-assisted development tools (e.g., Claude Code) to:
- Accelerate development
- Generate reusable assets
- Improve maintainability
- Ensure platform supports rapid iteration and long-term scalability
5. AI / NLP / RAG Integration
- Design and implement natural language interfaces for interacting with metrics
- Build and maintain RAG pipelines leveraging:
- Metric definitions
- Metadata and contextual information
- Develop prompt engineering strategies and query translation logic
- Enable workflows such as:
- “Ask a question → generate query → return visualization and explanation”
- Continuously improve AI output accuracy, usability, and relevance
6. Visualization & Self-Service Enablement
- Design and implement dynamic, user-configurable dashboards and visualizations
- Enable:
- Filtering, slicing, and drill-down analysis
- Customizable chart configurations
- Saved and shareable views
- Deliver export capabilities (PNG, CSV, PDF)
- Ensure intuitive and scalable self-service user experience
7. Documentation & Design Artifacts
- Develop and maintain:
- Metrics design specifications
- Data models and lineage documentation
- Architecture diagrams
- AI workflow and prompt design documentation
- Ensure documentation supports transparency, governance, and reuse
8. Agile / SAFe Delivery Execution
- Lead quarterly SAFe Program Increment (PI) planning participation and execution
- Define and manage:
- Epics, features, and user stories
- Partner with Scrum Master to:
- Plan and execute sprints
- Maintain and prioritize backlog
- Ensure continuous delivery aligned to program priorities and timelines
9. Cross-Functional Collaboration
- Collaborate with:
- AI Program leadership
- Business stakeholders
- Data and platform engineering teams
- Translate requirements into metrics, architecture, and implemented solutions
- Communicate outputs clearly to technical and non-technical audiences
10. Platform Evolution & Integration
- Design and evolve the platform to integrate with:
- Databricks
- Collibra
- Identify opportunities to:
- Enhance automation
- Improve usability
- Increase performance and scalability
- Continuously evaluate and adopt emerging AI and analytics capabilities
11. Governance, Quality & Performance
- Establish and enforce metrics governance processes
- Implement quality controls and validation rules for data and KPIs
- Monitor system usage and platform performance
- Ensure compliance with enterprise data, security, and governance standards
Required Qualifications
Education & Experience
- Bachelor’s or Master’s degree in Data Science, Computer Science, Analytics, or related field
- 6–10+ years of experience in data science, analytics engineering, or related field
- Proven experience owning the full lifecycle of metrics/KPI frameworks (definition through implementation)
- Experience building data products, analytics platforms, or metrics systems
- Experience working in Agile and/or SAFe environments
Technical Skills
- Data & Analytics
- Advanced SQL (complex queries, performance optimization)
- Strong Python for data processing and analytics
- Deep experience in data modeling and KPI design
- AI & Machine Learning
Experience with: - Large language models (Claude)
- Prompt engineering
- Retrieval-augmented generation (RAG)
- Vector search
-
Semantic query systems
-
Software Development
- Experience building data-driven applications and APIs
- Backend frameworks (Node.js, FastAPI, or similar)
-
Experience with front-end frameworks (React preferred)
-
Data Visualization
- Experience with charting libraries (ECharts, Recharts, D3) or BI tools
-
Strong data visualization and UX principles
-
Data Platforms (Preferred)
- Exposure to Databricks
- Experience with ETL/data pipeline frameworks
Key Competencies
- Strong systems thinking and architecture mindset
- Ability to own and execute across the full lifecycle of solutions
- Capability to translate business needs into scalable metrics and data solutions
- Balance between rapid prototyping and maintainable design
- Strong communication and stakeholder engagement skills
- Ownership mindset and comfort operating in ambiguity
- Continuous learning in AI, analytics, and emerging technologies
Summary
A hands-on data scientist responsible for the full lifecycle of AI metrics—defining, architecting, implementing, and evolving a modern, AI-powered analytics platform and metrics that enables self-service insights across the enterprise.
Position Overview
The Data Scientist, AI Metrics & Portal is a technical role responsible for owning the full lifecycle of AI Program metrics, including defining, architecting, implementing, operationalizing, and continuously improving a standardized AI metrics capability. This role combines data science, analytics engineering, artificial intelligence, and software development to: (1) Establish AI Program metrics—from conceptual definition through technical implementation and ongoing optimization, and (2) Design, build, and operate a modern, lightweight AI Metrics Hub, leveraging Claude Code and other tech stack tools to rapidly develop and maintain an extensible analytics platform. The Data Scientist will define and operationalize standardized AI metrics, architect the supporting data and application layers, implement dynamic visualization and AI-driven querying capabilities, and ensure continuous evolution of the platform to meet business needs. The role will orchestrate metrics design, platform engineering, and Agile delivery practices to:
- Define, standardize, and govern AI metrics across adoption, utilization, performance, value, cost, risk, and other categories.
- Architect scalable data models and metrics frameworks to ensure consistency and reuse.
- Implement and operationalize metrics pipelines, logic, and computation layers.
- Design and build an analytics platform with AI metrics catalog, standard/pre-configured AI dashboards, and self-service AI dashboards and exploration.
- Implement AI-powered natural language querying and discovery capabilities.
- Maintain and evolve metrics definitions, lineage, and supporting documentation.
- Deliver iteratively using Agile and SAFe methodologies.
- Enable continuous improvement and future integration with enterprise platforms (e.g., Databricks, Collibra).
This role requires a balance of hands-on implementation, architecture ownership, and delivery leadership, with accountability for the end-to-end lifecycle of AI metrics and insights capabilities.
Key Responsibilities
1. AI Metrics Lifecycle Ownership (Define → Architect → Implement → Operate → Evolve)
- Own the full lifecycle of AI metrics, including:
- Definition and standardization
- Architectural design
- Technical implementation
- Operational monitoring
- Continuous improvement
- Define and maintain a comprehensive AI metrics framework, including:
- Adoption, utilization, engagement
- Business value and ROI
- Performance and quality
- Risk, compliance, and cost
- Translate business questions into well-defined, implementable metrics and models
2. Metrics Architecture & Standardization
- Architect scalable, reusable metric models, including:
- KPI definitions and calculation logic
- Dimensional structures and aggregation strategies
- Establish and enforce standards for consistency, governance, and reuse
- Ensure metrics are designed for extensibility and enterprise integration
3. Metrics Implementation & Data Engineering
- Design and implement metrics computation pipelines and transformations
- Develop and maintain SQL and Python logic for KPI calculation
- Integrate and normalize data from multiple sources (logs, APIs, databases, surveys, risk reviews, and more)
- Ensure data accuracy, consistency, and performance optimization
- Implement data quality validation and monitoring processes
4. AI Metrics Portal Development
- Architect, build, and maintain the AI Metrics Hub application
- Develop platform components, including:
- Metrics registry (definitions, metadata, ownership)
- Dynamic dashboard and visualization engine
- Config-driven metric execution layer
- Leverage AI-assisted development tools (e.g., Claude Code) to:
- Accelerate development
- Generate reusable assets
- Improve maintainability
- Ensure platform supports rapid iteration and long-term scalability
5. AI / NLP / RAG Integration
- Design and implement natural language interfaces for interacting with metrics
- Build and maintain RAG pipelines leveraging:
- Metric definitions
- Metadata and contextual information
- Develop prompt engineering strategies and query translation logic
- Enable workflows such as:
- “Ask a question → generate query → return visualization and explanation”
- Continuously improve AI output accuracy, usability, and relevance
6. Visualization & Self-Service Enablement
- Design and implement dynamic, user-configurable dashboards and visualizations
- Enable:
- Filtering, slicing, and drill-down analysis
- Customizable chart configurations
- Saved and shareable views
- Deliver export capabilities (PNG, CSV, PDF)
- Ensure intuitive and scalable self-service user experience
7. Documentation & Design Artifacts
- Develop and maintain:
- Metrics design specifications
- Data models and lineage documentation
- Architecture diagrams
- AI workflow and prompt design documentation
- Ensure documentation supports transparency, governance, and reuse
8. Agile / SAFe Delivery Execution
- Lead quarterly SAFe Program Increment (PI) planning participation and execution
- Define and manage:
- Epics, features, and user stories
- Partner with Scrum Master to:
- Plan and execute sprints
- Maintain and prioritize backlog
- Ensure continuous delivery aligned to program priorities and timelines
9. Cross-Functional Collaboration
- Collaborate with:
- AI Program leadership
- Business stakeholders
- Data and platform engineering teams
- Translate requirements into metrics, architecture, and implemented solutions
- Communicate outputs clearly to technical and non-technical audiences
10. Platform Evolution & Integration
- Design and evolve the platform to integrate with:
- Databricks
- Collibra
- Identify opportunities to:
- Enhance automation
- Improve usability
- Increase performance and scalability
- Continuously evaluate and adopt emerging AI and analytics capabilities
11. Governance, Quality & Performance
- Establish and enforce metrics governance processes
- Implement quality controls and validation rules for data and KPIs
- Monitor system usage and platform performance
- Ensure compliance with enterprise data, security, and governance standards
Required Qualifications
Education & Experience
- Bachelor’s or Master’s degree in Data Science, Computer Science, Analytics, or related field
- 6–10+ years of experience in data science, analytics engineering, or related field
- Proven experience owning the full lifecycle of metrics/KPI frameworks (definition through implementation)
- Experience building data products, analytics platforms, or metrics systems
- Experience working in Agile and/or SAFe environments
Technical Skills
- Data & Analytics
- Advanced SQL (complex queries, performance optimization)
- Strong Python for data processing and analytics
- Deep experience in data modeling and KPI design
- AI & Machine Learning
Experience with: - Large language models (Claude)
- Prompt engineering
- Retrieval-augmented generation (RAG)
- Vector search
-
Semantic query systems
-
Software Development
- Experience building data-driven applications and APIs
- Backend frameworks (Node.js, FastAPI, or similar)
-
Experience with front-end frameworks (React preferred)
-
Data Visualization
- Experience with charting libraries (ECharts, Recharts, D3) or BI tools
-
Strong data visualization and UX principles
-
Data Platforms (Preferred)
- Exposure to Databricks
- Experience with ETL/data pipeline frameworks
Key Competencies
- Strong systems thinking and architecture mindset
- Ability to own and execute across the full lifecycle of solutions
- Capability to translate business needs into scalable metrics and data solutions
- Balance between rapid prototyping and maintainable design
- Strong communication and stakeholder engagement skills
- Ownership mindset and comfort operating in ambiguity
- Continuous learning in AI, analytics, and emerging technologies
Summary
A hands-on data scientist responsible for the full lifecycle of AI metrics—defining, architecting, implementing, and evolving a modern, AI-powered analytics platform and metrics that enables self-service insights across the enterprise.
ML Software Engineer Job Roles in Virginia
See all 443+ ML Software Engineer Jobs in Virginia
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Search ML Software Engineer Jobs in VirginiaML Software Engineer Jobs in Virginia: Frequently Asked Questions
Which companies sponsor visas for ML software engineers in Virginia?
Northern Virginia hosts some of the most active ML engineering sponsors in the country. Amazon Web Services (with its HQ2 presence in Arlington), Capital One, Booz Allen Hamilton, Leidos, and Accenture Federal Services regularly sponsor H-1B visas for ML roles. Smaller AI-focused firms and government contractors in the Reston and Tysons corridor also sponsor consistently, particularly for applied ML and NLP engineering positions.
Which visa types are most common for ML software engineer roles in Virginia?
The H-1B is by far the most common visa category for ML software engineers in Virginia, as ML engineering qualifies as a specialty occupation requiring at least a bachelor's degree in computer science, mathematics, or a related field. Some candidates at large multinational firms pursue L-1B visas for intracompany transfers. Graduates on OPT or STEM OPT frequently work in Virginia ML roles before transitioning to H-1B sponsorship.
How to find ml software engineer visa sponsorship jobs in Virginia?
Migrate Mate is built specifically for international candidates seeking visa sponsorship and lets you filter ML software engineer roles by state, so you can focus exclusively on Virginia employers actively sponsoring. Because many Virginia ML roles are tied to defense or federal contracting, filtering by employer type on Migrate Mate can help you identify which companies are actively filing sponsorship petitions for this role.
Which cities in Virginia have the most ML software engineer sponsorship jobs?
Northern Virginia, particularly Arlington, McLean, Reston, and Tysons, concentrates the vast majority of ML engineering sponsorship activity in the state. This corridor sits adjacent to Washington D.C. and hosts dense clusters of tech firms, federal contractors, and consultancies. Richmond has a growing presence, driven by Capital One's headquarters and a small but expanding fintech and data engineering sector, though it trails Northern Virginia significantly in volume.
Are there any Virginia-specific considerations for ML software engineer visa sponsorship?
A notable share of ML engineering roles in Virginia involve work with federal agencies or defense programs, which can require security clearances. Non-U.S. citizens are generally ineligible for most security clearances, so some positions in this corridor are effectively closed to sponsored candidates regardless of visa status. Focusing your search on commercial tech employers, financial services firms, and non-classified consulting practices improves your realistic pool of sponsoring opportunities.
What is the prevailing wage for sponsored ml software engineer jobs in Virginia?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.