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About Bosch
Bosch is a global engineering and technology company supplying mobility solutions, industrial technology, consumer goods, and energy and building technology; Robert Bosch LLC is its North American arm.
Bosch currently has
69 open roles
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Listed pay typically runs
$165,000–$185,000
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JOB TITLE: Artificial Intelligence / Machine Learning: Foundation Models - Intern
As an intern within the Foundation Model Powered AI Enablers group, you will conduct research and engineering focused on Foundation Models and Data-centric AI. Your daily responsibilities involve implementing research results to solve real-world challenges, ensuring high-quality system integration within existing platforms, and documenting findings for publications or patent submissions. You will work on specialized areas including Foundation Model AI tooling, spatiotemporal machine learning, time series learning, and general representation learning. The role requires proficiency in programming languages like Python and familiarity with platforms such as PyTorch. You will apply these skills to AIoT business domains, specifically addressing technical problems in automated driving, connected vehicles, Industry 4.0, and car multimedia systems to develop scalable, transparent, and intelligent analytic solutions.
Conduct research and engineering in AI and machine learning with a focus on Foundation Models and Data-centric AI.
Implement research results to solve real-world challenges across Bosch's AIoT business domains.
Ensure high-quality system integration of research results within existing Bosch platforms.
Develop scalable and transparent AI and big data analytic solutions for various industrial domains.
Document research findings for high-caliber publications and patent submissions.
Apply specialized knowledge in spatiotemporal machine learning, time series learning, and general representation learning.
Utilize machine learning platforms like PyTorch to develop vision and multimodality foundation models.
What we're looking for
Currently pursuing a Master's degree with peer-reviewed publications in machine learning, deep learning, or computer vision, or a Ph.D. in Computer Science.
Research experience or an equivalent graduate research background primarily in AI technologies including Foundation Models and Data-centric AI.
Publications in premier machine learning, deep learning, and computer vision journals and conferences.
Proficiency in one or more programming languages commonly used in machine learning, such as Python.
Minimum GPA of 3.0.
Knowledge in machine learning and deep learning, notably in Foundation Model AI tooling, ADAS, spatiotemporal machine learning, deep graph learning, general representation learning, and model distillation (preferred).
Familiarity with major machine learning platforms like PyTorch (preferred).
Experience in vision and multimodality foundation models and AI-based ADAS solutions (preferred).
Generative AI
PyTorch
Python
Multimodal Learning
Reinforcement Learning
Computer Vision
Distributed Training
World Models
Robotics
Simulation
Video Understanding
VLMs
Foundation Models
Action-conditioned Simulation
Policy Learning
GenAI
Foundation Models
Deep Learning
Python
C++
Natural Language Processing
Computer Vision
Diffusion Models
3D Reconstruction
World Models
Data Science
AI System Engineering
Time-series Analysis
Mixed Reality
IoT
ADAS
Auto-labeling
GenAI
Foundation Models
Deep Learning
Python
C++
Natural Language Processing
Computer Vision
Diffusion Models
3D Reconstruction
World Models
Data Curation
Auto-labeling
AI System Engineering
Time-series Analysis
Big Data Visual Analytics
Explainable AI
IoT
ADAS
Python
PyTorch
PyTorch Lightning
Timeseries Foundation Models
Deep Learning
Signal Processing
MLFlow
Git
Slurm
LSF
Natural Language Processing
Computer Vision
Data Science
Big Data
IoT
Synthetic Data Generation
Agentic AI
VSCode
PyCharm
Python
PyTorch
JAX
C++
Machine Learning
Deep Learning
Foundation Models
AI Safety
Generative World Models
Self-Supervised Learning
Distributed Training
XAI
Robotics
Computer Vision
Natural Language Processing
Data Science
Simulation Frameworks
OOD) detection
Python
PyTorch
JAX
TensorFlow
Deep Learning
Large Language Models
Reinforcement Learning
Pallas
Triton
Kernel Optimization
Data Research
On-policy Distillation