Senior Data Science Engineer Visa Sponsorship Jobs in Michigan
Michigan's senior data science engineer market centers on Detroit's automotive and mobility sector, with major employers like Ford, General Motors, and Stellantis sponsoring international talent for data-intensive roles. Ann Arbor's tech corridor and university connections add further opportunities, making the state a growing destination for senior-level data science professionals seeking visa sponsorship.
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At Ford, you’ll work on ideas that matter, alongside passionate people who want to make a global impact. Together, we’re shaping the next era of transportation—grounded in purpose, driven by progress. Make your move.
- Job Type: Full time
- Work Type: Hybrid
We are seeking an accomplished, hands-on Senior Software Engineer to lead the design and implementation of core artificial intelligence capabilities within our Intelligent Data Analytics Platform, with a particular emphasis on multi-agent orchestration and semantic search. This position is intended for a highly capable individual contributor who is able to operate effectively at both architectural and implementation levels — an engineer who anchors the team technically by producing production-grade code, resolving the most demanding problems, and establishing engineering standards by example.
The successful candidate will serve as a principal contributor to an AI-first platform that enables users to explore, query, and analyze enterprise BigQuery data through agentic tools and capabilities.
Architecture and System Design
- Contribute to the design of scalable, multi-agent AI architectures.
- Design components and modules across agent orchestration, tool systems, and large language model (LLM) integration.
- Evaluate trade-offs across architectural choices (e.g., single- versus multi-agent designs, retrieval-augmented generation versus fine-tuning, deterministic versus probabilistic pipelines).
- Participate in design reviews and contribute to Architecture Decision Records (ADRs).
Hands-On Engineering and Execution
- Produce production-grade code across agent frameworks, backend APIs, and frontend interfaces on a daily basis.
- Develop and evolve reusable AI components, including agent tools, embedding pipelines, and evaluation frameworks.
- Implement LLM-powered workflows, including natural-language-to-SQL generation, semantic search, and metadata enrichment.
- Develop services that enable intelligent data access, such as vector search, hybrid retrieval, and query scope management.
- Implement guardrails, validation layers, and observability mechanisms for AI-generated outputs.
Full-Stack Development
- Build performant backend services (Python/ FastAPI) and interactive frontend applications (Angular/React) for data exploration.
- Develop both conversational (chat) and structured (API) interfaces for analytical workloads.
- Construct evaluation and benchmarking tooling to support continuous measurement of AI quality.
- Assume end-to-end ownership of features, from initial design through deployment and ongoing monitoring.
Semantic Search and Embeddings
- Implement vector embedding pipelines for metadata discovery using pgvector.
- Develop semantic retrieval capabilities across datasets, tables, and columns, employing hybrid search strategies.
- Optimize search relevance through embedding strategies, re-ranking, and rigorous evaluation metrics.
- Contribute to the platform's data quality and governance capabilities.
Engineering Excellence
- Produce clean, maintainable, and scalable code that adheres to industry best practices.
- Participate actively in code reviews and establish quality standards through exemplary personal contributions.
- Conduct root-cause analysis on agent failures and implement systematic remediations.
- Serve as the team's technical anchor and primary point of reference for complex implementation challenges.
Collaboration
- Partner with Product, Data Engineering, and Platform teams to ensure successful feature delivery.
- Support colleagues through pair programming, knowledge sharing, and technical mentorship.
- Contribute to sprint planning, effort estimation, and technical feasibility assessments.
- Assist in onboarding new team members and disseminating domain expertise across the organization.
Required Qualifications
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
8 + years of professional software engineering experience with demonstrated hands-on coding proficiency.
Demonstrable experience building AI-powered applications or operating LLM-based systems in production environments.
Proven ability to interpret ambiguous requirements and independently deliver functional, well-tested software.
Strong debugging and problem-solving capabilities across the full technology stack.
A demonstrated record of owning and delivering complex features from inception through completion.
Technology Stack
Programming Languages and Frameworks: Python (primary), Java, JavaScript/TypeScript, Angular/React
Artificial Intelligence and Machine Learning: Google ADK, LangChain/LangGraph, OpenAI and Gemini APIs, prompt engineering, retrieval augmented generation (RAG) pipelines
Data and Cloud Infrastructure: Google Cloud Platform (BigQuery, Vertex AI, and Cloud Run preferred)
Backend Technologies: FastAPI, Pydantic, SQLModel/SQLAlchemy, PostgreSQL with pgvector
Frontend Technologies: Angular or React, TypeScript
Continuous Integration, Continuous Delivery, and Infrastructure: Terraform, GitHub Actions, Docker
Evaluation: Custom evaluation frameworks, LLM-as-judge methodologies
Preferred Qualifications
Experience with the Google Agent Development Kit (ADK) or comparable agent frameworks, such as CrewAI, or LangGraph.
Applied machine learning experience encompassing embeddings, classification, clustering, natural language processing, and evaluation metrics.
Demonstrated experience with vector databases and semantic retrieval optimization.
Familiarity with data engineering practices and data governance processes.
Prior experience developing internal developer tooling or platform SDKs.
Overview
At Ford, you’ll work on ideas that matter, alongside passionate people who want to make a global impact. Together, we’re shaping the next era of transportation—grounded in purpose, driven by progress. Make your move.
- Job Type: Full time
- Work Type: Hybrid
We are seeking an accomplished, hands-on Senior Software Engineer to lead the design and implementation of core artificial intelligence capabilities within our Intelligent Data Analytics Platform, with a particular emphasis on multi-agent orchestration and semantic search. This position is intended for a highly capable individual contributor who is able to operate effectively at both architectural and implementation levels — an engineer who anchors the team technically by producing production-grade code, resolving the most demanding problems, and establishing engineering standards by example.
The successful candidate will serve as a principal contributor to an AI-first platform that enables users to explore, query, and analyze enterprise BigQuery data through agentic tools and capabilities.
Architecture and System Design
- Contribute to the design of scalable, multi-agent AI architectures.
- Design components and modules across agent orchestration, tool systems, and large language model (LLM) integration.
- Evaluate trade-offs across architectural choices (e.g., single- versus multi-agent designs, retrieval-augmented generation versus fine-tuning, deterministic versus probabilistic pipelines).
- Participate in design reviews and contribute to Architecture Decision Records (ADRs).
Hands-On Engineering and Execution
- Produce production-grade code across agent frameworks, backend APIs, and frontend interfaces on a daily basis.
- Develop and evolve reusable AI components, including agent tools, embedding pipelines, and evaluation frameworks.
- Implement LLM-powered workflows, including natural-language-to-SQL generation, semantic search, and metadata enrichment.
- Develop services that enable intelligent data access, such as vector search, hybrid retrieval, and query scope management.
- Implement guardrails, validation layers, and observability mechanisms for AI-generated outputs.
Full-Stack Development
- Build performant backend services (Python/ FastAPI) and interactive frontend applications (Angular/React) for data exploration.
- Develop both conversational (chat) and structured (API) interfaces for analytical workloads.
- Construct evaluation and benchmarking tooling to support continuous measurement of AI quality.
- Assume end-to-end ownership of features, from initial design through deployment and ongoing monitoring.
Semantic Search and Embeddings
- Implement vector embedding pipelines for metadata discovery using pgvector.
- Develop semantic retrieval capabilities across datasets, tables, and columns, employing hybrid search strategies.
- Optimize search relevance through embedding strategies, re-ranking, and rigorous evaluation metrics.
- Contribute to the platform's data quality and governance capabilities.
Engineering Excellence
- Produce clean, maintainable, and scalable code that adheres to industry best practices.
- Participate actively in code reviews and establish quality standards through exemplary personal contributions.
- Conduct root-cause analysis on agent failures and implement systematic remediations.
- Serve as the team's technical anchor and primary point of reference for complex implementation challenges.
Collaboration
- Partner with Product, Data Engineering, and Platform teams to ensure successful feature delivery.
- Support colleagues through pair programming, knowledge sharing, and technical mentorship.
- Contribute to sprint planning, effort estimation, and technical feasibility assessments.
- Assist in onboarding new team members and disseminating domain expertise across the organization.
Required Qualifications
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
8 + years of professional software engineering experience with demonstrated hands-on coding proficiency.
Demonstrable experience building AI-powered applications or operating LLM-based systems in production environments.
Proven ability to interpret ambiguous requirements and independently deliver functional, well-tested software.
Strong debugging and problem-solving capabilities across the full technology stack.
A demonstrated record of owning and delivering complex features from inception through completion.
Technology Stack
Programming Languages and Frameworks: Python (primary), Java, JavaScript/TypeScript, Angular/React
Artificial Intelligence and Machine Learning: Google ADK, LangChain/LangGraph, OpenAI and Gemini APIs, prompt engineering, retrieval augmented generation (RAG) pipelines
Data and Cloud Infrastructure: Google Cloud Platform (BigQuery, Vertex AI, and Cloud Run preferred)
Backend Technologies: FastAPI, Pydantic, SQLModel/SQLAlchemy, PostgreSQL with pgvector
Frontend Technologies: Angular or React, TypeScript
Continuous Integration, Continuous Delivery, and Infrastructure: Terraform, GitHub Actions, Docker
Evaluation: Custom evaluation frameworks, LLM-as-judge methodologies
Preferred Qualifications
Experience with the Google Agent Development Kit (ADK) or comparable agent frameworks, such as CrewAI, or LangGraph.
Applied machine learning experience encompassing embeddings, classification, clustering, natural language processing, and evaluation metrics.
Demonstrated experience with vector databases and semantic retrieval optimization.
Familiarity with data engineering practices and data governance processes.
Prior experience developing internal developer tooling or platform SDKs.
Senior Data Science Engineer Job Roles in Michigan
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Search Senior Data Science Engineer Jobs in MichiganSenior Data Science Engineer Jobs in Michigan: Frequently Asked Questions
Which companies sponsor visas for senior data science engineers in Michigan?
Michigan's largest visa sponsors for senior data science engineers include Ford Motor Company, General Motors, Stellantis, and automotive supplier Aptiv, all of which regularly file H-1B visa petitions for data science roles. Beyond automotive, healthcare systems like Henry Ford Health and financial technology firms in Detroit's growing tech sector have also sponsored senior data science engineers. Ann Arbor companies tied to the University of Michigan research ecosystem are active sponsors as well.
Which visa types are most common for senior data science engineer roles in Michigan?
The H-1B is the most common visa for senior data science engineers in Michigan, as these roles typically qualify as specialty occupations requiring a bachelor's degree or higher in computer science, statistics, or a related field. Candidates with exceptional research credentials may pursue the O-1A. Some senior engineers sponsored by large multinational firms like Ford or GM may qualify for an L-1A or L-1B transfer if they're moving from an overseas office.
Which cities in Michigan have the most senior data science engineer sponsorship jobs?
Detroit and its surrounding metro area, including Dearborn and Auburn Hills, account for the largest share of senior data science engineer sponsorship activity in Michigan, driven by automotive and mobility employers headquartered there. Ann Arbor is a strong secondary hub, supported by University of Michigan spinoffs, health technology companies, and established tech employers. Grand Rapids has a smaller but growing presence, particularly in healthcare data and manufacturing analytics.
How to find senior data science engineer visa sponsorship jobs in Michigan?
Migrate Mate is built specifically for international job seekers and filters senior data science engineer roles in Michigan by verified visa sponsorship activity, saving you the work of screening employers manually. Because Michigan's sponsorship market is concentrated in automotive, healthcare, and university-adjacent tech, filtering by those sectors on Migrate Mate helps you focus on the most active sponsors. Setting up alerts for Michigan-based roles means you see new postings as soon as they appear.
Are there state-specific factors that affect senior data science engineer sponsorship in Michigan?
Michigan's concentration in automotive manufacturing means senior data science engineers here often work on vehicle data platforms, manufacturing analytics, or autonomous vehicle systems, which shapes the technical skills employers prioritize. Because many Michigan sponsors are large, established companies, their immigration programs tend to be well-resourced, though H-1B cap timing and prevailing wage determinations under Department of Labor rules apply equally here as anywhere in the U.S. The University of Michigan's OPT pipeline also feeds demand for senior-level talent conversion.
What is the prevailing wage for sponsored senior data science engineer jobs in Michigan?
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.