In this role, you will help explore how generative AI can advance sensing and interaction. You will research, develop, and evaluate generative models and AI systems for physical signals such as time series and images. Success here is measured by real-world impact on products and users, not by leaderboard numbers. That takes a deep understanding of the signals you model and statistical rigor in how you judge results. These are challenging, open-ended problems, and we are looking for someone with the persistence to carry an idea all the way into production.
You will also bring a solid understanding of physical signals and signal processing, and use it to develop sensing algorithms for products. You will collaborate with algorithm, firmware, and software engineers to deliver features from prototyping through production.
We value engineers who check their claims against real data, report negative results honestly, and explain technical findings clearly to people outside their field.
Minimum Qualifications
BS in Computer Science, Electrical Engineering, or a related STEM field with a minimum of 3 years of relevant industry or research experience
Hands-on experience developing generative AI models or systems that go beyond tutorials, such as through shipped products, publications, or substantial projects
Experience working with real-world sensor or physical measurement data. Strong understanding of physical signals such as time series and images, and how their structure and noise shape what a model learns
Solid foundation in digital signal processing and time-series analysis, such as filtering, spectral analysis, and feature extraction
Strong Python programming skills and proficiency with a modern deep learning framework such as PyTorch
Preferred Qualifications
MS or PhD in machine learning, signal processing, electrical engineering, physics, or a related field, with publications, open-source contributions, or shipped products in generative AI
Experience training generative or other ML models on time-series, sensor, audio, or image data, including at scale on GPU clusters
Statistical rigor in designing experiments and evaluations and in interpreting their results, backed by reproducible research practices
Experience using LLM-based AI assistants and agents to accelerate research and development work, such as coding, data analysis, and experiment automation
Proficiency in C/C++, and familiarity with embedded or resource-constrained systems
Demonstrated persistence in driving a challenging project from idea to production or real-world deployment
Excellent communication and cross-functional collaboration skills across algorithms, firmware, and software disciplines