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The Bosch Research and Technology Center North America - with offices in Pittsburgh, Pennsylvania, Sunnyvale, California, and Watertown, Massachusetts - is part of the global Bosch Group (www.bosch.com), a company with over 70 billion euro revenue, 400,000 people worldwide, a very diverse product portfolio, and a history of over 125 years. The Research and Technology Center North America (RTC-NA) is committed to providing technologies and system solutions for various Bosch business fields primarily in the areas of Robotics, Human Machine Interaction (HMI), Energy Technologies, Internet Technologies, Circuit Design, Semiconductors and Wireless, and MEMS Advanced Design.

Cyber-Physical AI and Reasoning (Engineer / Researcher)

The Cyber-Physical AI and Reasoning group at Bosch Research Pittsburgh develops intelligent systems that tightly integrate learning, reasoning, perception, and physical interaction. Our mission is to build safe, robust, and adaptive cyber-physical systems that operate reliably in real-world environments-spanning robotics, automation, manufacturing, and intelligent devices.

We focus on systems that combine data-driven learning with structured models, physical constraints, and embedded intelligence, enabling machines to sense, decide, and act across diverse scenarios while continuously improving over time, including through interaction with humans.

Core Research & Development Areas

Our work spans a broad range of Cyber-Physical AI topics, including but not limited to:

• Embodied and Cyber-Physical AI
  • Robot learning and control in physical environments
  • Dexterous manipulation and automation for manufacturing
  • Human-machine interaction and shared autonomy
• Hybrid and Model-Based AI
  • Combining learning-based models with physics-based, symbolic, or optimization-based components 
  • World models, state estimation, and system identification
  • Safety-aware and constraint-driven learning and control
• Multimodal & Foundation Models
  • Vision-Language(-Action) models for perception, planning, and control
  • Representation learning across modalities (vision, language, proprioception, signals)
  • Cross-domain and cross-embodiment generalization
• Cyber-Physical Systems & Embedded Intelligence
  • Embedded ML and edge AI for real-time systems
  • Integration of learning algorithms with sensors, actuators, and control stacks
  • Sim-to-real transfer and deployment on physical platforms
• Engineering & Prototyping
  • System prototyping
  • Data collection pipelines, simulation environments, and benchmarking frameworks
  • Deployment of AI systems to industrial settings

Role & Responsibilities

Depending on background and seniority, candidates will contribute to a mix of research and engineering activities, including:

• Defining and investigating compelling problems in Cyber-Physical AI & Reasoning
• Designing, implementing, and evaluating learning-based or hybrid AI systems
• Conducting literature reviews and translating insights into practical system designs
• Developing experimental pipelines (simulation, real-world testing, data collection)
• Analyzing system performance, robustness, safety, and failure modes
• Collaborating with interdisciplinary teams spanning AI, robotics, and engineering
• Contributing to:
  • Research publications and technical reports
  • Industrial patents and technology transfer
  • Prototypes deployed in labs or production environments

Technical Experience & Skills

We welcome candidates with overlapping subsets of the following skills-depth in all areas is not required:

• Cyber-Physical Systems & Robotics
  • State estimation, system modeling, or dynamics
  • Safety, robustness, or generalization in physical systems
  • Robot perception, control, planning, or manipulation
• Engineering & Systems
  • Embedded systems, real-time systems, or edge AI
  • Integration of ML models with hardware, sensors, and control software
  • Experience with simulation tools, robotics middleware, or control stacks
• Machine Learning & AI
  • Multimodal learning, representation learning, or foundation models
  • Reinforcement learning, imitation learning, or optimal control
  • Hybrid approaches combining data-driven and model-based methods (e.g., neuro-symbolic integration)
• Practical ML & Experimentation
  • Training and evaluating neural models (single- or multi-GPU)
  • Data curation, dataset analysis, and benchmarking
  • Debugging non-convex optimization and real-world system failures

Minimum Qualifications

• Master's or Ph.D. in Computer Science, Robotics, Electrical/Mechanical Engineering, Machine Learning, or a related field
• Strong foundation in AI/ML, cyber-physical systems, robotics, control
• Experience with programming and experimental system development

Preferred Qualifications

• Experience with physical or robotic hardware systems
• Experience with embedded or real-time systems
• Experience with multimodal foundation models
• Exposure to hybrid or model-based AI methods
• Prior research publications, technical reports, or strong project portfolios
• Experience collaborating in interdisciplinary or industrial research teams

Who Should Apply

This role is well-suited for:

• Early-career researchers seeking hands-on experience in Cyber-Physical AI
• Candidates interested in bridging AI research and real-world engineering
• Researchers and engineers excited about deploying AI systems beyond simulation

All your information will be kept confidential according to EEO guidelines.