Signal Processing Engineer II (Tools & Validation)

As a Signal Processing Engineer, focused on Deep Learning, you will be part of a cross‑functional team composed of Signal Processing, WHOOP Labs, Firmware, and Data Science. You will work on raw sensor data and the core, fundamental features at WHOOP. This role will be tasked with solving the incredibly difficult technical problem of obtaining physiological information from noisy sensor data and enhancing diagnostic tools and control methodologies.

RESPONSIBILITIES

Utilize expertise in signal processing and time‑series analysis to analyze biosensor systems and optimize their performance. Design and implement deep‑learning (DL) and machine‑learning (ML) models to extract valuable insights from large repositories of time‑series/biosensor data. Conduct experiments and perform rigorous testing of the models. Optimize and fine‑tune the DL/ML models for deployment in production systems, considering factors such as computational resources and real‑time constraints. Write clean, efficient, and maintainable code that is production ready. Stay up to date with the latest advancements in DL research and technologies. Monitor and ensure the proper functioning of algorithms across our diverse user population, addressing any issues related to data and data quality. Contribute to ongoing research efforts and explore new features to improve the overall performance of our products. Validate wearable technology in clinical settings, analyze biomedical data, and prepare comprehensive reports for cross‑functional teams. Join us in pushing the boundaries of wearable technology and positively impacting people's lives!

QUALIFICATIONS

PhD or Master’s degree in electrical engineering, biomedical engineering, computer science engineering, statistics, or a related field. Solid understanding of ML principles, algorithms, and particularly DL techniques. At the Signal processing team, we like to be aware of the mathematics behind the algorithms we use. Knowledge of and experience with signal processing, time‑series analysis, biosensor systems, and analyzing biomedical data Academic research experience in DL and ML with published articles or minimum of 2 years of industry experience with focus on DL/ML algorithms Proficient in Python, and familiar with ML/DL libraries like scikit‑learn, TensorFlow, PyTorch, Keras, etc. Excellent communication skills, both written and oral, to effectively convey complex technical concepts to diverse teams. Demonstrated ability to think innovatively and adapt to changing requirements while consistently producing high‑quality reports within tight deadlines. Experience with multiple DL architectures including CNNs, RNNs, transformers. Being conversant with state‑of‑the‑art DL and meta‑learning techniques is a significant plus. Experience with cloud computing resources is a plus. #J-18808-Ljbffr WHOOP