Entry-Level – AI/Machine Learning Researcher
$128k - $279k • Mountain View, California, United States of America • Full-time
Posted 19d ago
Job Location
Mountain View, California, United States of America
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Research Engineer
About the job
GM R&D is seeking an outstanding early-career researcher to contribute to advanced work in Artificial Intelligence, Machine Learning, and Large Language Models. This role is designed for a recent or soon-to-be-completed Ph.D. who brings strong depth in one or more advanced AI areas and the ability to work across disciplines. The researcher will develop proof-of-concepts (POCs), evaluate them on meaningful engineering or scientific problems, communicate findings clearly, and collaborate with senior researchers and engineering partners to move promising ideas toward impact. The work may span causal reasoning, neurosymbolic AI, world models, multimodal and generative AI, engineering agents, or related areas, connecting theory and experimentation with real engineering needs while maintaining high standards for reproducibility, explainability, uncertainty awareness, and responsible AI.
Responsibilities
- Conduct original research in advanced AI areas like causal AI, neurosymbolic AI, world models, multimodal or generative AI, or engineering agents.
- Translate engineering and scientific challenges into research questions, algorithms, and experimental plans.
- Design, implement, and evaluate machine learning models and proof-of-concepts using various data types.
- Develop methods for reasoning about physical systems, causality, uncertainty, constraints, and complex interactions.
- Establish baselines and evaluation protocols, analyzing failure modes, robustness, generalization, interpretability, and uncertainty.
- Build reproducible research code, data-processing workflows, and experiment pipelines using modern deep learning practices.
- Collaborate with researchers and engineers to understand domain constraints, validate results, and identify pathways for practical use.
- Communicate technical findings through reports, presentations, publications, and documentation.
- Contribute to research roadmaps by identifying promising approaches, risks, and high-value experiments.
- Participate in a collaborative research culture valuing curiosity, peer review, responsible innovation, and continuous learning.
Requirements
- Pursuit or completion of a PhD in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Computational Science, Engineering, Physics, or a related field.
- Demonstrated research depth in at least one relevant area, supported by dissertation, publications, patents, open-source work, or comparable contributions.
- Strong foundations in machine learning and deep learning, with working knowledge of optimization, probability/statistics, numerical methods, or scientific computing.
- Experience developing and evaluating models in Python using frameworks like PyTorch, JAX, or TensorFlow.
- Ability to formulate experiments, select metrics, analyze results, and communicate technical conclusions.
- Strong verbal and written communication skills for effective collaboration.
- Ability to work on-site or in a hybrid research environment.