Sobre este puesto de Senior Machine Learning Scientist (Sensor Intelligence) en Whoop
At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.
WHOOP is seeking a Senior Machine Learning Scientist to join the Sensor Intelligence Group (SIG), a cross-functional team collaborating across WHOOP Labs, Firmware, and Machine Learning and Research. This role focuses on developing compact machine learning models for edge deployment and is central to scaling AI systems that power WHOOP’s most foundational health features. In this role, you’ll develop next-generation, personalized AI from prototyping to productization, ultimately delivering personalized coaching to millions of WHOOP members.
RESPONSIBILITIES:
Research, prototype, and productize lightweight deep learning models suitable for resource-constrained edge targets
Drive deep learning model customization and compression strategies such as distillation, pruning, fine-tuning, and quantization-aware training
Collaborate with product teams to define member experience targets and with cloud-focused machine learning teams to implement distributed AI systems
Lead build/buy decisions by evaluating commercial and open-source model performance for WHOOP use cases
Stay current in Edge AI industry trends and best practices and mentor junior team members
QUALIFICATIONS:
Bachelor's degree in Computer Science, Electrical/Computer Engineering, Applied Mathematics, or a related field; Master’s or PhD degree preferred
5+ years of experience as a Machine Learning Scientist or similar role with a focus on advanced development, preferably related to voice and/or text-based conversational systems
Demonstrated experience training, fine-tuning, and deploying state-of-the-art deep learning architectures to resource-constrained embedded targets
Experience pre-training and fine-tuning small language models and/or building natural language understanding (NLU) models than run on resource-constrained targets
Experience with cloud platforms (AWS or GCP) and familiarity with modern MLOps practices such as CI/CD, model versioning, monitoring, and observability
Strong communication and collaboration skills across cross-functional teams
Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions
ADDITIONAL DESIRABLE EXPERIENCE:
Experience deploying deep learning models to microcontrollers or other resource-constrained edge devices using toolchains such as TFLite/LiteRT or ExecuTorch, and with inference libraries such as CMSIS-NN or CMSIS-DSP
Experience developing machine learning models for consumer-facing products
Experience building multi-modal datasets, including speech, video, text, or physiological signals, for human-AI interaction
Familiarity with time-series foundation models and self-supervised learning methods
