About the job
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 90 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) provides technologies and system solutions for various Bosch business fields, primarily in the field of artificial intelligence (AI), energy technologies, internet technologies, circuit design, semiconductors and wireless, as well as advanced MEMS design. Part of Bosch AI research in Pittsburgh, we are responsible for pushing the boundaries of multimodal sensing AI capabilities to solve complex industry problems and shape the future of Bosch products and services. We work with internal partners of different Bosch business units globally to transfer our solutions into future products as well as secure intellectual property (IP) for Bosch. We also actively collaborate with leading groups in academia (e.g., Carnegie Mellon University) and industry to promote research ideas and publish research findings in internationally renowned AI conferences and journals.
Responsibilities
Work together with lab director/leadership team to define and execute the research vision and roadmap for multimodal sensor foundation models, generative AI for temporal and spatial signals, and advanced signal processing–ML hybrids
Ensure research outcomes meet both scientific excellence and product relevance
Lead R&D portfolio involving machine learning on heterogeneous sensors (e.g., radar, audio, RF, IMU, vision, industrial sensors), including representation learning, self-supervised learning, and multimodal fusion to improve sensing and perception capabilities in a wide range of applications from automated vehicles, intelligent consumer products to manufacturing & industrial automation
Advance generative and probabilistic models for signals, including simulation, synthesis, forecasting, anomaly detection, and inverse problems
Maintain a team culture of scientific/technical excellence as evidenced by high impact IPs and/or publications in top AI conferences and journals (e.g., NeurIPS, ICLR, ICML, CVPR, ICASSP)
Collaborate with academic partners (e.g., CMU) and represent the group in the broader research community
Foster entrepreneurial research, establish rigorous SW engineering practices towards translating research into production-ready artifacts
Live by ROI mindset: mapping R&D targets to product roadmap and potential market opportunities
Partner closely with product, engineering, and business teams to deploy AI at scale
Balance long-term research with near- and mid-term business impact
Support technology transfer, IP generation, and patent strategy
Lead, mentor, and grow a team of PhD-level researchers and senior engineers
Manage budget/resources and secure team competency demands from internal stakeholders
Foster a culture of scientific rigor, collaboration, inclusion, and execution excellence
Recruit top research and engineering talent globally
Provide technical and career mentorship to team members
Qualifications
Minimum
PhD (or equivalent research experience) in Computer Science, Electrical Engineering, Applied Mathematics, Statistics specializing Machine Learning, Signal processing or a related field
5+ years of experience in AI research and development, with demonstrated commercialization or product deployment of ML systems
3+ years of prior team leadership or management experience, leading engineers and/or researchers in a corporate research environment
Strong background in machine learning for signals, such as:
Multimodal learning and sensor fusion
High-frequency signal modeling and representation learning
Signal processing combined with deep learning
Self-supervised, foundation, or generative models
Proven track record of peer-reviewed publications in top ML and/or signal processing venues
Strong communication skills and the ability to influence across research, engineering, and business stakeholders
Preferred
Experience building and deploying foundation models or large-scale representation learning systems for sensor data in automotive/industrial settings
Exposure to automotive, industrial, manufacturing, robotics, or consumer tech. sensing AI applications
Experience with real-time, embedded, or edge AI systems
Track record of patents or technology transfer in an industrial setting
Experience managing cross-site or cross-disciplinary teams