Staff Artificial Intelligence Machine Learning Engineer

General Motors is seeking a Staff AI/ML Engineer for the Vehicle Mechatronic Embedded Controls (VMEC) Analytics team. The role focuses on delivering production AI/ML solutions for diagnostics and prognostics, requiring hands-on experience in designing and operating machine learning systems.

Responsibilities:

• 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:

• 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
• 10+ years of applied machine learning or data science experience, including ownership of high-impact, production AI systems
• Experience with vehicle, fleet, or telematics data, or adjacent domains with rich time-series and reliability data
• Background in diagnostics/prognostics modeling (e.g., fault classification, anomaly detection, degradation modeling, survival analysis)
• Experience building vector search and retrieval-augmented generation (RAG) or similar production AI applications that integrate foundation models with structured data
• Familiarity with Azure Cognitive Services or similar managed AI services and how to combine them pragmatically with custom ML for robust production solutions
• Demonstrated impact in raising engineering standards and building AI/ML engineering capability across teams
• Prior experience in automotive, embedded controls, or software-defined vehicle programs, or other safety-critical domains