Research Scientist - Cyber-Physical AI & Reasoning

Company DescriptionWe Are Bosch.At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our areas of activity are every bit as diverse as our outstanding Bosch teams around the world. Their creativity is the key to innovation through connected living, mobility, or industry. Let’s grow together, enjoy more, and inspire each other. Work #LikeABoschReinvent yourself: At Bosch, you will evolve. Discover new directions: At Bosch, you will find your place. Balance your life: At Bosch, your job matches your lifestyle. Celebrate success: At Bosch, we celebrate you. Be yourself: At Bosch, we value values. Shape tomorrow: At Bosch, you change lives.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 (), 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.Job DescriptionCyber-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 AreasOur work spans a broad range of Cyber-Physical AI topics, including but not limited to:Embodied and Cyber-Physical AIRobot learning and control in physical environmentsDexterous manipulation and automation for manufacturingHuman–machine interaction and shared autonomyHybrid and Model-Based AICombining learning-based models with physics-based, symbolic, or optimization-based components World models, state estimation, and system identificationSafety-aware and constraint-driven learning and controlMultimodal & Foundation ModelsVision-Language(-Action) models for perception, planning, and controlRepresentation learning across modalities (vision, language, proprioception, signals)Cross-domain and cross-embodiment generalizationCyber-Physical Systems & Embedded IntelligenceEmbedded ML and edge AI for real-time systemsIntegration of learning algorithms with sensors, actuators, and control stacksSim-to-real transfer and deployment on physical platformsEngineering & PrototypingSystem prototypingData collection pipelines, simulation environments, and benchmarking frameworksDeployment of AI systems to industrial settingsRole & ResponsibilitiesDepending 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 & ReasoningDesigning, implementing, and evaluating learning-based or hybrid AI systemsConducting literature reviews and translating insights into practical system designsDeveloping experimental pipelines (simulation, real-world testing, data collection)Analyzing system performance, robustness, safety, and failure modesCollaborating with interdisciplinary teams spanning AI, robotics, and engineeringContributing to:Research publications and technical reportsIndustrial patents and technology transferPrototypes deployed in labs or production environmentsQualificationsTechnical Experience & SkillsWe welcome candidates with overlapping subsets of the following skills—depth in all areas is not required:Cyber-Physical Systems & RoboticsState estimation, system modeling, or dynamicsSafety, robustness, or generalization in physical systemsRobot perception, control, planning, or manipulationEngineering & SystemsEmbedded systems, real-time systems, or edge AIIntegration of ML models with hardware, sensors, and control softwareExperience with simulation tools, robotics middleware, or control stacksMachine Learning & AIMultimodal learning, representation learning, or foundation modelsReinforcement learning, imitation learning, or optimal controlHybrid approaches combining data-driven and model-based methods (e.g., neuro-symbolic integration)Practical ML & ExperimentationTraining and evaluating neural models (single- or multi-GPU)Data curation, dataset analysis, and benchmarkingDebugging non-convex optimization and real-world system failuresMinimum QualificationsMaster’s or Ph.D. in Computer Science, Robotics, Electrical/Mechanical Engineering, Machine Learning, or a related fieldStrong foundation in AI/ML, cyber-physical systems, robotics, controlExperience with programming and experimental system developmentPreferred QualificationsExperience with physical or robotic hardware systemsExperience with embedded or real-time systemsExperience with multimodal foundation modelsExposure to hybrid or model-based AI methodsPrior research publications, technical reports, or strong project portfoliosExperience collaborating in interdisciplinary or industrial research teamsWho Should ApplyThis role is well-suited for:Early-career researchers seeking hands-on experience in Cyber-Physical AICandidates interested in bridging AI research and real-world engineeringResearchers and engineers excited about deploying AI systems beyond simulationAdditional InformationAll your information will be kept confidential according to EEO guidelines.SummaryType: Full-timeFunction: Engineering