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‑defined vehicle programs, or other safety‑critical domains.
