Staff AI Engineer

General Motors is seeking a Staff AI Engineer to design and deliver trustworthy AI systems and data products end to end. This onsite role in Warren, MI focuses on turning ambiguous business needs into measurable AI objectives, building production-ready capabilities, and operating them reliably across Sales, Service, Marketing, and Global Markets.

What you will do

• Translate unclear business problems into defined AI objectives, solution strategies, evaluation plans, and measurable success criteria.
• Develop, validate, and improve predictive and forecasting solutions, including classification, segmentation, optimization, natural-language, generative-AI, and decision-support approaches.
• Select practical statistical, machine-learning, and AI methods aligned to the decision, available data, operational constraints, risk, and expected value.
• Apply modern AI patterns when appropriate, including experimentation, time-series forecasting, causal analysis, retrieval-augmented generation, and agentic workflows.
• Partner with stakeholders to determine when AI is the right approach versus simpler analytics, rules, or workflow automation.
• Define data requirements and work with data engineering, platform, and business teams to establish reliable, documented data sources.
• Build scalable data capabilities across ingestion, transformation, aggregation, features, embeddings, and knowledge pipelines using batch or streaming patterns.
• Create reusable data products, analytical datasets, semantic models, feature assets, and retrieval indexes for multiple use cases.
• Conduct exploratory analysis and data-quality work such as schema validation, feature selection, leakage detection, and data-availability analysis across structured and unstructured sources.
• Work with customer, vehicle, dealer, sales, service, warranty, incentive, document, conversation, and operational datasets.
• Deliver production-grade AI applications, services, APIs, assistants, dashboards, and workflow tools so AI capabilities are usable by end users.
• Design interfaces connecting data pipelines, models, foundation models, tools, backend services, and front-end or workflow applications.
• Implement retrieval, grounding, tool use, orchestration, prompt management, evaluation, and human-in-the-loop patterns where they fit.
• Develop secure, testable, observable, and maintainable software with code review, version control, and automated testing.
• Collaborate with architects and software engineers on structure, integration patterns, authentication, authorization, deployment, and operational support.
• Establish evaluation frameworks tied to real-world business outcomes, then assess quality factors such as calibration, bias, interpretability, robustness, factuality, groundedness, safety, usability, latency, and operational fit.
• Design and analyze experiments, pilots, red-team exercises, and champion/challenger validation to manage risk before broad adoption.
• Deploy as APIs, batch processes, scheduled workflows, applications, assistants, or decision-support capabilities, with strong practices for versioning, lineage, prompt and model tracking, release management, rollback, and reproducibility.
• Implement monitoring for data quality, drift, model and response quality, safety signals, application health, latency, availability, usage, and business outcomes.
• Troubleshoot production issues, identify root causes, and improve models, pipelines, services, prompts, and user experiences through iterative refinement.
• Apply responsible-AI, privacy, security, and data-governance principles, including controls for access, auditability, explainability, and human oversight.
• Document assumptions, limitations, intended use, prohibited use, evaluation results, and operational ownership; coordinate with security, privacy, architecture, legal, and governance teams for compliant enterprise deployment.
• Act as a technical authority across applied AI, machine learning, data engineering, application development, and AI platform patterns, raising quality through reusable patterns, reference implementations, documentation, testing, observability, and reproducibility.
• Coach data scientists, analysts, engineers, and citizen builders on modeling, data, software development, and responsible-AI practices.
• Support teams evaluating enterprise tooling such as Databricks, Azure AI, Azure AI Foundry, Glean, and other platforms when they improve delivery without sacrificing quality or governance.
• Share lessons learned, reusable components, reference architectures, evaluation methods, and practical guidance across the AI Center and partner organizations.

What you bring

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field (advanced degree preferred).
• 8+ years of professional experience across AI, machine learning, data science, data engineering, software engineering, application development, or a related discipline.
• Proven experience moving AI, machine-learning, analytics, or data products from problem definition and proof of concept through production deployment and ongoing operations.
• Strong Python and SQL skills, including production-quality code, testing, version control, documentation, and debugging.
• Hands-on experience with tools such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent libraries.
• Experience building or integrating AI applications, APIs, services, data pipelines, dashboards, and workflow tools.
• Experience with model development, feature engineering, experiment design, statistical analysis, AI evaluation, and communicating results to nontechnical audiences.
• Experience deploying and operating solutions via APIs, batch pipelines, notebooks-to-production workflows, applications, assistants, or similar production patterns.
• Practical understanding of software and data engineering practices including CI/CD, automated testing, observability, dependency management, data quality, and release management.
• Experience with large-scale data platforms such as Databricks, Spark/PySpark, cloud data warehouses, or equivalent technologies.
• Ability to work independently, make sound technical tradeoffs, and deliver in a fast-changing cross-functional environment.

Technologies

• Python, SQL, Pandas, NumPy, scikit-learn, PyTorch, TensorFlow
• Databricks, Spark, PySpark
• CI/CD, Azure AI, Azure AI Foundry, Glean
• REST APIs, MLflow

Preferred qualifications

• Master’s or PhD in Computer Science, Statistics, Machine Learning, Operations Research, Mathematics, Engineering, or a related quantitative field.
• Experience in automotive, sales, service, marketing, customer analytics, dealer analytics, warranty, incentives, forecasting, or other operationally complex domains.
• Experience building internal AI platforms, developer tools, data applications, decision-support products, assistants, or enterprise workflow solutions.
• Experience with large language models including prompt engineering, retrieval-augmented generation, embeddings, vector search, tool calling, agentic workflows, or AI application evaluation.
• Experience with causal inference, time-series forecasting, optimization, recommendation systems, natural-language processing, or generative-AI-enabled workflows.
• Experience with MLflow or comparable tools for experiment tracking, model registry, evaluation, and lifecycle management.
• Experience with Azure, Azure AI Foundry, Databricks, REST APIs, containerized deployment, CI/CD, cloud-native data services, and application observability.
• Experience defining model governance, responsible-AI controls, data governance, security controls, interpretability practices, or risk-based evaluation standards.
• Experience quantifying financial impact and partnering with Finance or business leaders to validate value realization.
• Familiarity with enterprise AI and developer platforms such as Glean, Azure AI Foundry, Databricks, GitHub, or comparable platforms.

Immigration / sponsorship: GM does not provide immigration-related sponsorship for this role. Do not apply if you will need GM immigration sponsorship now or in the future.

Remote work / reporting requirement: This role is based remotely; however, if the selected candidate lives within a specific mile radius of a GM hub, they are expected to report to the location three times a week (or at another frequency directed by their manager).

Relocation: This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.