GM's AI Research team is pioneering how cutting-edge machine learning can transform the way vehicles are designed, manufactured, and experienced by building the next generation of intelligent systems.
Requirements
- In depth knowledge about modern deep learning architectures—Transformers, Diffusion Models, CNNs and model training techniques at scale
- Strong hands-on experience with at least one of the popular AI/ML frameworks (PyTorch, Tensorflow, Keras or JAX)
- Strong programming skills in Python and familiarity with one or more of systems languages (C++/Java)
- Demonstrated track record of publications in top AI/ML conferences or patents demonstrating novel contributions to the field
- Ability to formulate research questions from ambiguous problems and apply rigorous experimental methodology including hypothesis formation, evaluation, and statistical analysis
- Experience with anomaly detection and predictive maintenance applications through course work, research or projects
- Experience with reinforcement learning for robotic control or process optimization through course work, research or projects
Responsibilities
- Adapt machine learning architectures for complex industrial applications, including computer vision, robotic manipulation, predictive maintenance, and process optimization
- Build end-to-end deep learning pipelines that handle multi-modal sensor data (vision, force/torque, proprioception, environmental sensors)
- Contribute to the development of foundation models and transfer learning frameworks that generalize across diverse industrial scenarios and equipment types
- Contribute to the development of data collection and annotation strategies to build high quality datasets for training and validating models in industrial settings
- Work with partner teams to translate technical requirements into ML solutions and support integration efforts
- Own the deployment and monitoring of ML models in production environments
- Publish research findings in top-tier venues (for example, NeurIPS, ICML, CVPR) and contribute to GM’s presence in the research community
Other
- This role is categorized as hybrid. This means the successful candidate is expected to report to the office three times per week or any other frequency dictated by the business.
- PhD in relevant field or related discipline (STEM focused) or Masters degree with significant ongoing AI/ML contributions
- Able to work full time, 40 hours per week
- Experience training multimodal deep learning models
- Demonstrated research contributions in AI/ML technologies through publication of PhD research in top- tier conferences or journals
