Senior Research Engineer, ML Lead, Health Frontiers

• 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.

Minimum qualifications:

• 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.

Preferred qualifications:

• 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 (including handling data noise and training dynamics).
• 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.

• 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.