AI/ML Researcher for Real-World R&D Impact

General Motors; Mountain View (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

General Motors R&D is seeking a PhD-level researcher to conduct original AI/ML research that accelerates engineering and product development. You will develop proof-of-concepts, evaluate them on meaningful problems, and collaborate with senior researchers and engineering partners to move ideas toward impact.

The role emphasizes research depth, cross-disciplinary collaboration, reproducibility, and responsible AI practices, with opportunities to contribute to world-models, neurosymbolic and

Qualifications

• Pursuit or completion of a PhD in Computer Science, AI, ML, or related fields.
• Able to work full-time, 40 hours per week.
• Demonstrated depth in at least one relevant AI area.
• Strong foundations in ML, optimization, statistics, or scientific computing.
• Experience developing and evaluating models in Python with ML frameworks.

Responsibilities

• Conduct original research in advanced AI areas (causal AI, neurosymbolic AI, world models, multimodal/generative AI).
• Translate questions into research hypotheses, algorithms, and plans.
• Design, implement, and evaluate ML models and POCs with real or simulated data.
• Develop methods to reason about physical systems, uncertainty, and complex interactions.
• Build reproducible research code, data-processing workflows, and experiments.
• Communicate findings through reports, presentations, and publications.

Skills

Python

PyTorch

JAX

TensorFlow

Research depth

Education

PhD in Computer Science / AI / ML

Tools

Python

PyTorch

JAX

TensorFlow

Job description

General Motors R&D is seeking a PhD-level researcher to conduct original AI/ML research that accelerates engineering and product development. You will develop proof-of-concepts, evaluate them on meaningful problems, and collaborate with senior researchers and engineering partners to move ideas toward impact.

The role emphasizes research depth, cross-disciplinary collaboration, reproducibility, and responsible AI practices, with opportunities to contribute to world-models, neurosymbolic and