Staff Artificial Intelligence Machine Learning Engineer

General Motors

Staff AI/ML Engineer at General Motors creating end-to-end AI/ML solutions for diagnostics and analytics. Collaborating with technical teams to deliver production-grade machine learning systems.

Posted 6/12/2026full-timeMilford • California, Missouri, Texas • 🇺🇸 United StatesLeadWebsite

Tech Stack

Tools & technologies

AzureCloudPythonPyTorchSparkSQLTensorflow

About the role

Key responsibilities & impact

Design, build, and operate end‑to‑end AI/ML solutions (data pipelines, models, services, and tools) for diagnostics, prognostics, and test analytics.
Implement production‑grade ML pipelines on platforms such as Azure and Databricks, covering data ingestion, feature engineering, training, evaluation, and inference for batch and streaming workloads.
Develop and maintain robust, observable ML services and internal tools that make complex vehicle and field data easy to use for engineers and technical stakeholders.
Apply practical ML and statistical methods (e.g., tree‑based models, time‑series and anomaly detection, deep learning where appropriate) with a focus on reliability, explainability, and impact.
Own model and data observability in production, including metrics, dashboards, alerts, and remediation workflows for drift, data quality, and performance regressions.
Partner with data engineering to define and use industrialized and vectorized data products that support search, RAG, and analytics at scale.
Review designs and code, mentor AI/ML practitioners, and help set high standards for testing, logging, deployment, and documentation.
Collaborate with diagnostics/prognostics SMEs, validation, safety, and program teams to prioritize work, define success metrics, and embed solutions in day‑to‑day engineering workflows.

Requirements

What you’ll need

Graduate degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, Statistics, Engineering, or a closely related quantitative field.
7+ years of hands‑on experience designing, building, and operating machine learning systems in production environments.
Strong proficiency in Python (production‑quality code, testing, packaging) and SQL, with experience working in shared, multi‑developer codebases.
Practical experience with core ML frameworks such as PyTorch, TensorFlow, or scikit‑learn, and with MLOps tooling (e.g., MLflow, CI/CD, model registries, experiment tracking).
Experience building data and ML workloads on cloud platforms, preferably Microsoft Azure, and working with Databricks, Spark, or similar distributed processing frameworks.
Demonstrated ability to turn ambiguous real‑world problems into shippable AI/ML solutions, owning the details from data exploration through deployed service and ongoing operation.
Strong understanding of ML system behavior in production (data issues, non‑stationarity, latency, throughput, failure modes) and comfort debugging with logs, metrics, and traces.
Excellent communication and collaboration skills, with a track record of influencing decisions and mentoring other AI/ML practitioners.

Benefits

Comp & perks

From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions.

ATS Keywords

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Hard Skills & Tools

machine learningdata pipelinesfeature engineeringmodel evaluationdeep learningPythonSQLMLOpsPyTorchTensorFlow

Soft Skills

communicationcollaborationmentoringinfluencing decisionsproblem-solvingreliabilityexplainabilityimpact focusdebuggingprioritization

Certifications

Master’s degreePhD