Staff Artificial Intelligence Machine Learning Engineer Job at General Motors (Austin)

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.

Job Responsibility

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

Nice to have

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

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Staff Artificial Intelligence Machine Learning Engineer

Job Description

Job Responsibility

Requirements

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