Staff AI Engineer

General Motors

. Translate ambiguous business problems into AI objectives, solution strategies, evaluation plans, and measurable success criteria .

Posted 10/23/2026full-timeWarren • Michigan • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AI and machine learning, with a strong focus on developing and deploying production-grade applications and data solutions. Proficient in Python, SQL, and relevant technologies, while applying responsible AI principles and effective data governance.

Highest-signal resume keywords

Python ProficiencySQL ProficiencyAI Application DevelopmentData EngineeringMachine Learning Model Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModelingData Quality AssessmentFeature EngineeringStatistical AnalysisExperiment DesignNatural Language ProcessingGenerative AIOptimizationSegmentationClassification

Soft Skills

Technical AuthorityCoachingIndependent OperationCross-Functional CollaborationCommunication

Tools & Technologies

DatabricksAzure AIPyTorchTensorFlowPandasNumPyScikit-learnCI/CDREST APIsCloud Data Warehouses

Industry Keywords

Artificial IntelligenceMachine LearningData ScienceData GovernanceAutomotive AnalyticsCustomer AnalyticsOperational ComplexityResponsible AIData EngineeringApplication Development

Tech Stack

Tools & technologies

AzureCloudNumpyPandasPySparkPythonPyTorchScikit-LearnSparkSQLTensorflow

About the role

Key responsibilities & impact

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
Define data requirements and establish reliable, documented data sources with data engineering, platform, and business teams
Build scalable 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
Build production-grade AI applications, services, APIs, assistants, dashboards, and workflow tools
Implement retrieval, grounding, tool use, orchestration, prompt management, evaluation, and human-in-the-loop patterns
Develop secure, testable, observable, and maintainable software using code review, version control, and automated testing
Establish evaluation frameworks 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
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
Monitor data quality, drift, model performance, response quality, safety signals, application health, latency, availability, usage, and business outcomes
Investigate production issues, identify root causes, and improve models, pipelines, services, prompts, and user experiences
Apply responsible-AI, privacy, security, and data-governance principles and define appropriate controls
Document assumptions, limitations, intended use, prohibited use, evaluation results, and operational ownership
Serve as a technical authority and trusted advisor across applied AI, machine learning, data engineering, application development, and AI platform patterns
Raise quality through reusable patterns, reference implementations, code reviews, documentation, testing, observability, and reproducibility
Coach data scientists, analysts, engineers, and citizen builders
Help teams evaluate and use Databricks, Azure AI, Azure AI Foundry, Glean, and other enterprise tooling
Share lessons learned, reusable components, reference architectures, evaluation methods, and practical guidance

Requirements

What you’ll need

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
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 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
Understanding of CI/CD, automated testing, observability, dependency management, data quality, and release management
Experience with 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
GM does not provide immigration-related sponsorship; applicants must not need GM immigration sponsorship now or in the future
Advanced degree preferred
Preferred experience in automotive, sales, service, marketing, customer analytics, dealer analytics, warranty, incentives, forecasting, or other operationally complex domains
Preferred experience with large language models, prompt engineering, retrieval-augmented generation, embeddings, vector search, tool calling, agentic workflows, or AI application evaluation
Preferred experience with causal inference, time-series forecasting, optimization, recommendation systems, natural-language processing, or generative-AI-enabled workflows
Preferred experience with MLflow, Azure, Azure AI Foundry, Databricks, REST APIs, containerized deployment, CI/CD, cloud-native data services, application observability, model governance, responsible-AI controls, data governance, security controls, interpretability practices, or risk-based evaluation standards

Benefits

Comp & perks

Benefits overview and Total Rewards resources
Opportunities supporting employee well-being at work and at home
Reasonable accommodations for job seekers with disabilities
No relocation benefits; relocation costs are the selected candidate’s responsibility