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