Staff AI Engineer

General Motors • Warren, MI

4d • Remote

About The Position

Designs and delivers trustworthy AI systems, machine-learning solutions, data products, and applications that improve decisions, automate work, and create measurable business impact across Sales, Service, Marketing, and Global Markets. This hands-on Staff AI Engineer role is for an experienced individual contributor who can work from business problem framing and analytical discovery through data engineering, model and AI-system development, application delivery, and production operation. The role combines practical data science with software and platform-engineering skills to build reusable, secure, maintainable, and widely adopted AI capabilities.

The engineer develops applied AI and machine-learning solutions; builds scalable data, feature, embedding, and knowledge pipelines; delivers production-grade AI applications and services; and establishes evaluation, reliability, monitoring, and governance practices. They also serve as a technical authority, coach colleagues, and share reusable patterns and guidance across the AI Center and partner organizations.

The position is based remotely. Candidates living within a specified radius of a GM hub are expected to report to that location three times per week or at another frequency set by their manager. The role is not eligible for relocation benefits. GM does not provide immigration-related sponsorship for this role.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• 8+ years of professional experience across artificial intelligence, machine learning, data science, data engineering, software engineering, application development, or a closely related discipline.
• Demonstrated experience taking AI, machine-learning, analytics, or data-product solutions from problem definition and proof of concept through production deployment and ongoing operation.
• Strong proficiency in Python and SQL, including production-quality code, testing, version control, documentation, and debugging.
• Hands-on experience with data-science and machine-learning libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent technologies.
• Experience building or integrating AI applications, APIs, services, data pipelines, applications, dashboards, or 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 through APIs, batch pipelines, notebooks-to-production workflows, applications, assistants, or comparable production patterns.
• Practical understanding of software and data engineering practices, including CI/CD, automated testing, observability, dependency management, data quality, and release management.
• Experience working with large-scale data platforms such as Databricks, Spark/PySpark, cloud data warehouses, or equivalent technologies.
• Demonstrated ability to operate independently, make sound technical tradeoffs, and deliver in a fast-changing, cross-functional environment.

Nice To Haves

• Advanced degree preferred; the preferred-qualifications section specifically lists a 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, 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, including Glean, Azure AI Foundry, Databricks, GitHub, or comparable platforms.

Responsibilities

• Translate ambiguous business problems into AI objectives, solution strategies, evaluation plans, and measurable success criteria.
• Develop, validate, and improve predictive, forecasting, classification, segmentation, optimization, natural-language, generative-AI, and decision-support solutions.
• Select statistical, machine-learning, and AI techniques based on business decisions, available data, operational constraints, risk, and expected value; determine when simpler analytics, rules, or workflow automation are more appropriate.
• Partner with data engineering, platform, and business teams to define data requirements and establish reliable, documented data sources.
• Build scalable batch or streaming ingestion, transformation, aggregation, feature, embedding, and knowledge pipelines.
• Create reusable data products, analytical datasets, semantic models, feature assets, and retrieval indexes for multiple use cases.
• Perform exploratory analysis, data-quality assessment, schema validation, feature selection, leakage detection, and data-availability analysis across structured and unstructured datasets.
• Build production-grade AI applications, services, APIs, assistants, dashboards, and workflow tools for end users.
• Translate models and AI systems into user experiences, decision-support tools, automation workflows, and self-service capabilities.
• Design interfaces between 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 appropriate.
• Develop secure, testable, observable, and maintainable software using code review, version control, and automated testing; partner on application structure, integration, authentication, authorization, deployment, and operational support.
• Establish evaluation frameworks tied to business outcomes and assess quality, calibration, bias, interpretability, robustness, factuality, groundedness, safety, usability, latency, and operational fit.
• Design and analyze experiments, pilots, red-team exercises, and champion/challenger approaches to validate value and manage risk.
• Deploy solutions as APIs, batch processes, scheduled workflows, applications, assistants, or decision-support capabilities.
• Establish dependency management, versioning, data lineage, prompt and model tracking, release management, rollback, and reproducibility practices.
• Design monitoring for data quality, drift, model performance, response quality, safety signals, application health, latency, availability, usage, and business outcomes.
• Investigate production issues, identify root causes, and iteratively improve models, pipelines, services, prompts, and user experiences.
• Apply responsible-AI, privacy, security, and data-governance principles throughout solution design and operation; define controls for access, data handling, auditability, explainability, human oversight, model and prompt risk, and third-party services.
• Document assumptions, limitations, intended and prohibited use, evaluation results, and operational ownership; partner with security, privacy, architecture, legal, and governance stakeholders on compliant enterprise deployment.
• Serve as a technical authority and trusted advisor on applied AI, machine learning, data engineering, application development, and AI platform patterns.
• Improve engineering quality through reusable patterns, reference implementations, code reviews, documentation, testing, observability, and reproducibility.
• Coach data scientists, analysts, engineers, and citizen builders on modeling, data, software-development, and responsible-AI practices.
• Help teams evaluate and use platforms such as Databricks, Azure AI, Azure AI Foundry, and Glean, and share lessons, components, architectures, evaluation methods, and guidance across the AI Center and partner organizations.

Benefits

• Benefits supporting employee well-being at work and at home are referenced; details are provided through GM Total Rewards resources, but specific benefits are not listed in the posting.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior