AI Research Intern – Time Series Foundation Models

Company Description

The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania, and Cambridge, Massachusetts is a part of the global Bosch Group (www.bosch.com), a company with over 70 billion euro revenue, 400,000 employees worldwide, a very diverse product portfolio, and a history spanning over 125 years. The Research and Technology Center North America (RTC-NA) is dedicated to providing technologies and system solutions for various Bosch business fields, primarily in the field of artificial intelligence, energy technologies, internet technologies, circuit design, semiconductors and wireless, as well as advanced MEMS design.

As a part of the global research, our AI research in Silicon Valley focuses on Foundation Models, Big Data Visual Analytics, Explainable AI (XAI), Natural Language Processing, Computer Vision & Mixed Reality, Cloud Robotics, Data Science, AI System Engineering, Time-series Analysis. We develop scalable, intelligent, and trustworthy AIoT solutions for Bosch products and services in application areas such as automated driving, advanced driver assistance systems (ADAS), robotics, smart manufacturing, enterprise AI, health care, smart home and building solutions.

Job Description

We are looking for research interns interested in advancing foundation models for multimodal and sensor-rich environments. Our research explores how AI systems can learn generalizable representations from heterogeneous real-world data, including time series, audio, vision, and physical sensor signals, and transfer that knowledge across tasks and domains.

As an intern, you will work alongside Bosch researchers on open research problems at the intersection of multimodal learning, foundation models, and real-world sensing. You will have access to challenging datasets and applications from areas such as mobility, manufacturing, robotics, and intelligent systems, with opportunities to turn research ideas into publications and working prototypes.

What You’ll Work On

Your research may explore topics such as:

Foundation models for time-series and Multimodal data

Causal Analysis and evidence-based reasoning

Learning shared representations across images, time series, and other sensing modalities

Self-supervised learning from large-scale unlabeled sensor datasets

Multimodal fusion and cross-modal representation learning

Pre-training strategies for heterogeneous and asynchronous data

Generative modeling and synthetic data for sensor-rich applications

Adapting pretrained models to new sensors, tasks, and domains with limited labeled data

Robust learning under missing modalities, noisy measurements, and distribution shifts

Efficient multimodal models for real-world and edge deployment

New architectures and evaluation methods for multimodal foundation models

You will formulate research questions, develop and evaluate new methods, build experimental prototypes, and work with Bosch researchers and domain experts to understand how these approaches behave on real-world problems. Strong research results may lead to publications at leading ML, AI, or signal-processing venues.

Qualifications

Basic Qualifications:

Currently pursuing a Ph.D. in Computer Science, Electrical/Computer Engineering, Machine Learning, AI, or a related field

Research experience with time-series foundation models, multimodal models, or self-supervised learning

Hands-on experience with PyTorch or similar frameworks

Research or project experience with multimodal learning, time-series modeling, representation learning, foundation models, or signal processing

Comfortable designing experiments and analyzing model behavior rather than only applying existing models

Strong Python programming skills

Ability to communicate research ideas clearly and work collaboratively

Preferred Qualifications:

Experience working with real-world sensor and telemetry signals from radar, LiDAR, IMU, or other multivariate time series

Experience with Transformers, generative models, contrastive learning, or cross-modal learning

Experience adapting large pretrained models through fine-tuning, parameter-efficient methods, or transfer learning

Familiarity with robustness, domain adaptation, missing-modality learning, or uncertainty estimation

Publications at venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICASSP, AAAI, or related conferences

Open-source implementations or research projects demonstrating strong experimental skills

Additional Information

Equal Opportunity Employer, including disability / veterans

*Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date.

BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics)

FIRST Robotics (For Inspiration and Recognition of Science and Technology)

AWIM (A World In Motion)

The U.S. base salary range for this intern position is $55.00-$70.00 hourly. Within the range, individual pay is determined based on several factors, including, but not limited to, type of degree, work experience and job knowledge, complexity of the role, type of position, job location, etc. Your Hiring Manager can share more details about the specific salary range for this position during the interview process.

For more information on our culture and benefits, please visit:

Culture and Benefits | Bosch in the USA