Requirements:

Bachelor’s degree or equivalent practical experience.
5 years of experience in machine learning research or research engineering, including experience leading technical projects.
3 years of experience training, adapting, or evaluating large-scale foundation models, and building data pipelines for heterogeneous datasets.
3 years of experience with modern machine learning frameworks (e.g., JAX, PyTorch, TensorFlow) and distributed training on accelerators.
3 years of experience designing evaluations, metrics, and controlled ablations for research projects.

Nice to Haves:

Master's degree or PhD in Computer Science or related technical field.
Experience modeling longitudinal or multimodal real-world data (e.g., audio, wearable sensor data, health records).
Experience profiling and debugging distributed training on TPU or GPU clusters.
Domain knowledge for health or fitness, combined with experience in scientific study design and causal inference.
Record of influential research, deployed ML systems, open-source contributions, or technical leadership in an advanced ML organization.

Design, train, and evaluate machine learning models, owning the full experimentation loop.
Develop automated-research agents capable of running quantitative evaluations, generating hypotheses, and executing computational experiments.
Develop evaluation frameworks that test scientific reasoning, temporal understanding, calibration, generalization, data leakage, and real-world utility.
Work with scientists to translate research questions into measurable endpoints and experimental designs, and provide technical leadership through architecture reviews and mentoring.
Provide decisive technical leadership by taking ownership in team settings, actively steering technical agendas, and making concrete decisions to overcome technical stalemates.