Staff Artificial Intelligence Machine Learning EngineerGeneral Motors • Austin, Texas, United States
Staff Artificial Intelligence Machine Learning Engineer
General Motors
- Austin, Texas, United States
- Austin, Texas, United States
About
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.
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 (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.
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 (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.
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 (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.
Benefits Overview From day one, we’re looking out for your well‑being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.
Non‑Discrimination and Equal Employment Opportunities (U.S.) All employment decisions are made on a non‑discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
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Languages
- English
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