Staff Artificial Intelligence Machine Learning Engineer

Job Description

The Role:

General Motors is seeking a Staff AI/ML Engineer for the Vehicle Mechatronic Embedded Controls (VMEC) Analytics team. 

The team delivers production AI/ML solutions for high‑impact diagnostics, prognostics, and test‑effectiveness use cases. This is a hands‑on practitioner role focused on building, shipping, and operating real systems - not on academic research. 

The Staff AI/ML Engineer will serve as a senior individual contributor within an established AI/ML leadership group, providing deep technical expertise, shaping implementation approaches, and mentoring others while collaborating on overall strategy. 

What You’ll Do:

• 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. 

Your Skills & Abilities (Required Qualifications):

• 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. 

What Will Give You A Competitive Edge (Preferred Skills):

• 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‑define