Our culture is focused on building inclusive teams, where differences and unique perspectives are embraced so you can contribute to your fullest potential as you pursue your career. Our locations feature a variety of work environments, including open work spaces and virtual connection platforms to inspire productivity and flexible collaboration. And we are proud to support our employees volunteer interests, and make it a priority to join together in efforts that give back to our communities. 

This is a unique opportunity to get in on the ground floor and help lead a multi-disciplined team of experienced individuals who will be driving the solution design, development, and deployment of advanced statistical and mathematical solutions using machine learning techniques as well as traditional methods. The team will generate new algorithmic and software models enabling enterprise tools for automated and autonomous driving systems development. 

• You should have deep familiarity with predictive modeling techniques based off time series analysis with very high dimensional data sets, especially regarding kinematics. 
• Candidates should be familiar with techniques from scientific computing such as function interpolation, numerical linear algebra, and Monte Carlo methods. 
• Additionally, candidates should be familiar with methods to identify latent class features in both labeled and label-free data, such as Indian buffet processes and topological data analysis. 
• Candidates should be familiar with relationships between algorithm runtime performance and hardware and software systems that contribute to latency, demand, and failures in distributed inference and training jobs. 

 The candidate will design and lead iterative development and learning cycles in these areas, and ultimately will produce new and creative solutions that will become part of the operating fabric of the global enterprise. The scope of the landscape may include: 

Candidates must be collaborative team players who will work closely with fellow IT professionals (scientists, developers, and product leads.) The candidate should have 7+ years’ experience across a broad set of potential roles, including but not limited to: 

•  Research Scientist 
• AI Analyst 
• Developer 
• Architect 

 Additional desired technical expertise: 

•  Analysis and modeling of telematics data, especially from a physical perspective 
• Manipulating high-volume, high-dimensionality data from varying sources to highlight patterns, anomalies, relationships, and trends with machine learning techniques 
• Modeling data abstractions employing multiple non-linear mathematical and statistical modeling approaches 
• Applying machine learning methodology, with emphasis on applying and integrating machine learning with traditional analytic methods 

 Development experience in one or more of the following areas: 

•  Python proficiency required 
• CPU frameworks for machine learning in python – numpy, pandas, sklearn 
• GPU frameworks for machine learning in python – Rapids, Pyro 
• Deep learning frameworks in python - PyTorch, Caffe, TensorFlow 
• Modern hyperscale ML technologies and pipelines - Kubernetes, Spark, DASK, Docker, Argo, MLFlow, NVIDIA Triton 

Education: 

General Motors is committed to being a workplace that is not only free of discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that workforce diversity creates an environment in which our employees can thrive and develop better products for our customers. We understand and embrace the variety through which people gain experiences whether through professional, personal, educational, or volunteer opportunities. 

We encourage interested candidates to review the key responsibilities and qualifications and apply for any positions that match your skills and capabilities.