below marketmost similar roles pay hereabove market
This role pays less than
63%
of similar roles. Most pay
$185,218–$254,750
— the blue band above.
At the midpoint, this role pays about
$204k
versus about
$220k
for comparable roles.
Based on 240 similar postings.
Employer
About General Motors (GM)
General Motors (GM) is a leading American multinational automotive corporation founded in 1908 and headquartered in Detroit, Michigan.
General Motors (GM) currently has
196 open roles
on FindRole.
Listed pay typically runs
$159,200–$245,000
across 54 roles with salary data.
The Entry-Level – AI/Machine Learning Researcher joins the GM R&D team to conduct original research and develop practical methods that accelerate engineering and product development. This role is designed for a recent Ph.D. to build research prototypes, develop proof-of-concepts, and evaluate models on scientific problems. The researcher will translate complex challenges into hypotheses and algorithms, focusing on areas such as causal reasoning, neurosymbolic AI, world models, multimodal and generative AI, and engineering agents. Key responsibilities include building reproducible research code, data-processing workflows, and experiment pipelines. Required skills include proficiency in Python and frameworks like PyTorch, JAX, or TensorFlow, alongside foundations in optimization, probability, and numerical methods. The work involves connecting theoretical research to real-world engineering needs while ensuring reproducibility, explainability, and responsible AI practices.
Conduct original research in advanced AI areas like causal AI, neurosymbolic AI, and generative models.
Translate engineering and scientific challenges into well-defined research questions, hypotheses, and experimental plans.
Design, implement, and evaluate machine learning models and proof-of-concepts using real or simulated data.
Develop methods to reason about physical systems, causal relationships, uncertainty, and temporal evolution.
Establish evaluation protocols to analyze failure modes, robustness, generalization, and interpretability.
Build reproducible research code, data-processing workflows, and experiment pipelines using modern deep learning practices.
Communicate technical findings through research reports, presentations, publications, and clear documentation.
Identify promising approaches and high-value experiments to contribute to technical research roadmaps.
What we're looking for
Pursuit or completion of a PhD in Computer Science, AI, Machine Learning, Applied Mathematics, Statistics, Computational Science, Engineering, Physics, or a related field.
Demonstrated research depth in a relevant area supported by a dissertation, publications, patents, open-source work, or comparable technical contributions.
Strong foundations in machine learning and deep learning, including optimization, probability/statistics, numerical methods, or scientific computing.
Experience developing and evaluating models in Python using frameworks such as PyTorch, JAX, or TensorFlow.
Ability to formulate experiments, select metrics, analyze results, and communicate technical conclusions to technical and general audiences.
Must be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee, or refugee) due to export control restrictions.
Research experience in causal inference, counterfactual reasoning, neurosymbolic methods, or world models (preferred).
Experience with multimodal AI, large language models, agentic systems, or AI-assisted engineering (preferred).
Artificial Intelligence
Machine Learning
Python
Deep Learning
Generative AI
Large Language Models
Computer Vision
Time-series Modeling
Optimization
PyTorch
TensorFlow
scikit-learn
Keras
MATLAB
R
SQL
MLOps
Digital Twins
Reinforcement Learning
Data Pipelines
Machine Learning
Deep Learning
Reinforcement Learning
Quantitative Finance
Statistics
Mathematics
Operations Research
AI
Portfolio Construction
Valuation Models