General Motors
AV Safety Engineering Analytics AI/ML Engineer (GPSSC)
Rol remoto de Machine Learning Engineer con fit claro de ubicación del candidato.
Publicado14 jun 2026
Países elegibles38 países aceptados
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Resumen del rol
AV Safety Engineering Analytics AI/ML Engineer (GPSSC)
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
What You’ll Do
- Contribute to the development of data analytics infrastructure that supports safety assurance analytics addressing internal and external stakeholder needs across the phases of automated vehicle development and deployment, including both real-world and simulation data.
- Apply your AI/ML expertise to developing trustworthy and explainable methods for validating the safety performance of an AI/ML based automated driving system.
- Mentor and develop team members in the appropriate implementation of AI/ML to support the mission of the SAFE-ADS department and AV Safety Engineering Analytics team.
- Pilot and develop metrics for monitoring of development operations and deployment, and establish sufficiency criteria for launch readiness.
- Develop methods for leveraging a variety of internal and external data sources for AI/ML based safety monitoring and contribute to the development of a reliable supply chain of continuously flowing data from a variety of sources (internal and external) to support safety assurance related activities.
- Implement cloud-based continuous up-time analytics solutions for monitoring driving performance for safety and generating browser based interactive visualizations and periodic reporting artifacts.
- Actively contribute to determining appropriate use of AI/ML approaches within automated driving systems and provide technical expertise to inform leadership decision-making regarding mechanisms for ensuring they are trustworthy and explainable.
- Through AI/ML expertise, contribute to the definition of GM’s data sourcing and processing strategy for AV safety assurance needs, engage externally to influence evolving standards, and contribute to internal and external thought leadership that strengthens GM’s position in the autonomous vehicle ecosystem.
- Represent SAFE-ADS in AI/ML related discussions across Global Product Safety, Systems, and Certification activities.
- Identify and drive opportunities to improve the efficiency, quality and transparency of safety analytics within GPSSC and across GM.
Your Skills & Abilities (Required Qualifications)
- Master’s degree in Computer Science, Mechanical Engineering, Vehicle Engineering, Physics, or a related field; or equivalent practical experience focused on AI/ML
- 10+ years of experience in large scale analyses of vehicle related data
- 5+ years in safety-critical AI/ML systems in automotive engineering applications
- Experience in the following:Machine Learning & AI: Extensive experience in building large-scale models with significant focus on E2E validation. Experience using Large Language Models (LLMs), Generative AI, RAG, Deep learning, Reinforcement Learning, Natural Language Processing (NLP), SVM, XGBoost, Random Forest, Decision Trees, ClusteringAI Standards and Evolving Regulations: Understanding of ISO/PAS 8800, NIST AI Risk Management Framework, EU AI Act (2024-2027), other applicable industry standards and best practices for autonomous vehicles, aerospace and/or robotics.Programming & Frameworks: Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, LangChain, SQLCloud & Big Data: Experience in cloud-based large scale process including notifications, queuing, serverless cloud functions, event driven processing, code as infrastructure, containerization, process monitoring, process optimization, identity and access management, service to service access, etc. (Microsoft Azure - Data Lake, Machine Learning, Databricks), (AWS - S3, SageMaker, Bedrock) or Google Cloud Platform (BigQuery, Dataflow, AI Platform)Deployment & MLOps: CI/CD, MLflow, Model Monitoring & Versioning, Docker & Kubernetes, GitHub, Jira, Jenkins, Poetry, TerraformData Analysis & Visualization: Tableau, PowerBI, Plotly/Dash, Shiny, Pandas, NumPy
- Machine Learning & AI: Extensive experience in building large-scale models with significant focus on E2E validation. Experience using Large Language Models (LLMs), Generative AI, RAG, Deep learning, Reinforcement Learning, Natural Language Processing (NLP), SVM, XGBoost, Random Forest, Decision Trees, Clustering
- AI Standards and Evolving Regulations: Understanding of ISO/PAS 8800, NIST AI Risk Management Framework, EU AI Act (2024-2027), other applicable industry standards and best practices for autonomous vehicles, aerospace and/or robotics.
- Programming & Frameworks: Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, LangChain, SQL
- Cloud & Big Data: Experience in cloud-based large scale process including notifications, queuing, serverless cloud functions, event driven processing, code as infrastructure, containerization, process monitoring, process optimization, identity and access management, service to service access, etc. (Microsoft Azure - Data Lake, Machine Learning, Databricks), (AWS - S3, SageMaker, Bedrock) or Google Cloud Platform (BigQuery, Dataflow, AI Platform)
- Deployment & MLOps: CI/CD, MLflow, Model Monitoring & Versioning, Docker & Kubernetes, GitHub, Jira, Jenkins, Poetry, Terraform
- Data Analysis & Visualization: Tableau, PowerBI, Plotly/Dash, Shiny, Pandas, NumPy
- Proven track record providing technical leadership in AI/ML applied to safety-critical systems
- Excellent communication and collaboration skills, with the ability to work effectively in a team environment
- Strong problem-solving mindset and a proactive attitude towards learning and self-improvement
Experience in the following:
- Machine Learning & AI: Extensive experience in building large-scale models with significant focus on E2E validation. Experience using Large Language Models (LLMs), Generative AI, RAG, Deep learning, Reinforcement Learning, Natural Language Processing (NLP), SVM, XGBoost, Random Forest, Decision Trees, Clustering
- AI Standards and Evolving Regulations: Understanding of ISO/PAS 8800, NIST AI Risk Management Framework, EU AI Act (2024-2027), other applicable industry standards and best practices for autonomous vehicles, aerospace and/or robotics.
- Programming & Frameworks: Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, LangChain, SQL
- Cloud & Big Data: Experience in cloud-based large scale process including notifications, queuing, serverless cloud functions, event driven processing, code as infrastructure, containerization, process monitoring, process optimization, identity and access management, service to service access, etc. (Microsoft Azure - Data Lake, Machine Learning, Databricks), (AWS - S3, SageMaker, Bedrock) or Google Cloud Platform (BigQuery, Dataflow, AI Platform)
- Deployment & MLOps: CI/CD, MLflow, Model Monitoring & Versioning, Docker & Kubernetes, GitHub, Jira, Jenkins, Poetry, Terraform
- Data Analysis & Visualization: Tableau, PowerBI, Plotly/Dash, Shiny, Pandas, NumPy
Your Skills & Abilities (Required Qualifications)
- Record of involvement in public AI/ML related discourse through conference participation or publications.
- Experience productionizing the use of AI/ML within the corporate setting.
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