Staff AI Engineer

GM•Warren, MI

1d•Remote

About The Position

The Staff AI Engineer is a hands-on senior individual-contributor role responsible for designing and delivering 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. The role spans business problem framing, analytical discovery, data engineering, model and AI-system development, application delivery, and production operation. It combines practical data science with software and platform engineering to build reusable, secure, maintainable, and widely adopted AI capabilities. The engineer develops AI and machine-learning solutions, builds data and feature pipelines, and delivers production-grade applications, services, APIs, assistants, dashboards, and workflow tools. They establish evaluation, reliability, monitoring, and responsible-AI practices; collaborate with cross-functional stakeholders; and provide technical leadership through reusable patterns, coaching, and practical guidance. The role is remote, with an expectation to report to a GM hub three times a week or at another frequency set by the manager if the selected candidate lives within a specified radius of a hub. 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.
At least 8 years of professional experience in 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 coding, 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, 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.
Ability to operate independently, make sound technical tradeoffs, and deliver in a fast-changing, cross-functional environment.

Nice To Haves

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 appropriate statistical, machine-learning, and AI methods based on business needs, data, operational constraints, risk, and expected value; determine when simpler analytics, rules, or workflow automation are preferable.
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.
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, translating AI capabilities into accessible user experiences and self-service tools.
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 architecture, integration, authentication, authorization, deployment, and operational support.
Establish evaluation frameworks tied to real-world 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 and response quality, safety, application health, latency, availability, usage, and business outcomes; investigate production issues and improve solutions through structured iteration.
Apply responsible-AI, privacy, security, and data-governance principles, including controls for access, data handling, auditability, explainability, human oversight, model and prompt risk, and third-party services.
Document assumptions, limitations, intended and prohibited uses, evaluation results, and operational ownership; collaborate with security, privacy, architecture, legal, and governance stakeholders on compliant deployment.
Serve as a technical authority and trusted advisor in 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 assess and use platforms such as Databricks, Azure AI, Azure AI Foundry, Glean, and other enterprise tools; share reusable components, reference architectures, evaluation methods, and practical guidance.