Staff Artificial Intelligence Machine Learning Engineer

General Motors (GM) is making strides in the realm of artificial intelligence and machine learning by seeking a Staff AI/ML Engineer for its Vehicle Mechatronic Embedded Controls (VMEC) Analytics team. This position is pivotal as it aims to enhance the company's capabilities in delivering production-ready AI and ML solutions that address high-impact diagnostics, prognostics, and test-effectiveness use cases.

The role is designed for a hands-on practitioner, emphasizing the importance of building, shipping, and operating real systems over engaging in academic research. The Staff AI/ML Engineer will be a senior individual contributor within an established AI/ML leadership group, tasked with providing deep technical expertise, shaping implementation strategies, and mentoring fellow team members while collaborating on broader strategic initiatives.

Responsibilities and Expectations

The Staff AI/ML Engineer will be responsible for designing, building, and operating end-to-end AI/ML solutions. This includes creating data pipelines, models, services, and tools specifically for diagnostics, prognostics, and test analytics. The role requires implementing production-grade machine learning pipelines on cloud platforms such as Azure and Databricks, which involves data ingestion, feature engineering, training, evaluation, and inference for both batch and streaming workloads.

A significant aspect of this position is the development and maintenance of robust, observable machine learning services and internal tools. These tools are essential for making complex vehicle and field data accessible to engineers and technical stakeholders. The engineer will apply practical machine learning and statistical methods, such as tree-based models, time-series analysis, anomaly detection, and deep learning, with a focus on reliability, explainability, and impact.

Moreover, the engineer will own the model and data observability in production, which includes monitoring metrics, creating dashboards, setting alerts, and establishing remediation workflows for issues such as data drift, quality, and performance regressions. Collaboration with data engineering teams will also be crucial to define and utilize industrialized and vectorized data products that support search, retrieval-augmented generation (RAG), and analytics at scale.

The role also entails reviewing designs and code, mentoring other AI/ML practitioners, and helping to set high standards for testing, logging, deployment, and documentation. Close collaboration with subject matter experts in diagnostics and prognostics, as well as validation, safety, and program teams, will be necessary to prioritize work, define success metrics, and integrate solutions into daily engineering workflows.

Qualifications and Experience

Candidates for this position are expected to possess a graduate degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, Statistics, Engineering, or a closely related quantitative field. A minimum of seven years of hands-on experience in designing, building, and operating machine learning systems in production environments is required. Strong proficiency in Python and SQL is essential, along with experience in shared, multi-developer codebases.

Practical experience with core machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn, as well as familiarity with MLOps tooling, is also necessary. The ideal candidate will have experience building data and ML workloads on cloud platforms, particularly Microsoft Azure, and working with distributed processing frameworks like Databricks and Spark.

Additionally, the role requires a demonstrated ability to transform ambiguous real-world problems into deployable AI/ML solutions, overseeing the details from data exploration to ongoing operation. A strong understanding of machine learning system behavior in production, including issues related to data, latency, and failure modes, is crucial. Excellent communication and collaboration skills are also vital, as the engineer will need to influence decisions and mentor other practitioners.

Why it matters

The search for a Staff AI/ML Engineer at General Motors highlights the growing importance of artificial intelligence and machine learning in the automotive industry. As vehicles become increasingly complex and data-driven, the need for skilled professionals who can create reliable and effective AI solutions is paramount. This role not only emphasizes technical expertise but also the ability to mentor and lead within a collaborative environment, which is essential for fostering innovation.

Moreover, GM's commitment to a vision of Zero Crashes, Zero Emissions, and Zero Congestion underscores the transformative potential of AI in creating safer and more efficient vehicles. By integrating advanced analytics and machine learning into their engineering workflows, GM aims to enhance vehicle performance and reliability, ultimately benefiting consumers and the environment.

In conclusion, the Staff AI/ML Engineer position represents a significant opportunity for professionals in the field to contribute to cutting-edge developments in automotive technology. As companies like GM continue to invest in AI and machine learning capabilities, the implications for creators and technologists are profound, paving the way for a future where intelligent systems play a central role in everyday transportation.