Join General Motors in a Staff AI Engineer role that focuses on designing and delivering trustworthy AI systems and data products end-to-end. This onsite position in Warren, MI supports scalable, secure, maintainable, and widely adopted AI capabilities across Sales, Service, Marketing, and Global Markets by combining data science depth with strong software and platform engineering.
What you’ll do
You’ll translate ambiguous business needs into clear AI objectives, including evaluation plans and measurable success criteria. You’ll then develop, validate, and improve predictive and decision-support solutions spanning forecasting, classification, segmentation, optimization, natural-language and generative-AI, and other AI patterns.
- Define when AI is the best path versus analytics, rules, or workflow automation for better outcomes.
- Select practical statistical, machine-learning, and AI techniques aligned to business value, data availability, operational constraints, risk, and expected value.
- Use modern approaches where they fit, including experimentation, time-series forecasting, causal analysis, retrieval-augmented generation, and agentic workflows.
- Partner with data engineering, platform, and business teams to establish reliable, documented data sources and data requirements.
- Build scalable ingestion, transformation, aggregation, feature, embedding, 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.
- Perform exploratory analysis and data-quality assessment, including schema validation, feature selection, leakage detection, and data-availability analysis across structured and unstructured data.
- Develop production-grade AI applications, services, APIs, assistants, dashboards, and workflow tools for end-user adoption.
- Design clean 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 appropriate.
- Deliver secure, testable, observable, and maintainable software with code review, version control, and automated testing.
- Design evaluation frameworks based on real-world business outcomes, and assess quality attributes such as calibration, bias, interpretability, robustness, factuality, groundedness, safety, usability, latency, and operational fit.
- Deploy solutions through APIs, batch processes, scheduled workflows, applications, assistants, or decision-support capabilities, including monitoring for data quality, drift, performance, response quality, safety signals, health, latency, availability, usage, and business outcomes.
- Investigate production issues, diagnose root causes, and iterate across models, pipelines, services, prompts, and user experiences.
- Apply responsible-AI, privacy, security, and data-governance practices, including access controls, auditability, explainability, human oversight, and risk-based controls for external or third-party services.
- Document assumptions, limitations, intended and prohibited use, evaluation results, and operational ownership.
Requirements
- 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 closely related disciplines.
- Proven 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 libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent technologies.
- 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.
- 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.
Preferred qualifications
- Master’s or PhD in a related quantitative field.
- Domain experience in automotive, sales, service, marketing, customer/dealer analytics, warranty, incentives, or forecasting.
- 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 and lifecycle management.
- Experience with Azure, Azure AI Foundry, Databricks, REST APIs, containerized deployment, CI/CD, cloud-native data services, and application observability.
- Experience with 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.
Tools and technologies
Python, SQL, Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Databricks, Spark, PySpark, CI/CD, Azure AI, Azure AI Foundry, Glean, REST APIs, MLflow
Location, remote work, and relocation
- Location: Warren, MI (onsite)
- Remote work / reporting: This role is based remotely; if you live within a specific mile radius of a GM hub, you will be expected to report to the location three times a week (or other frequency dictated by your manager).
- Relocation: Not eligible for relocation benefits; relocation costs are the responsibility of the selected candidate.
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.
