To create a foundation model of the brain, capturing the relationship between perception, cognition, behavior, and the activity dynamics of the brain, using large-scale multimodal foundation models and recent advances in neurotechnology and machine learning.
Requirements
- Master's degree in Computer Science or related field with 2+ years of relevant industry experience, OR Bachelor's degree with 4+ years of relevant industry experience
- 2+ years of practical experience in implementing and optimizing machine learning algorithms with distributed training using common libraries (e.g. Ray, DeepSpeed, HF Accelerate, FSDP)
- Strong programming skills in Python, with expertise in machine learning frameworks like TensorFlow or PyTorch
- Experience with orchestration platforms
- Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services
- Familiarity with MLOps platforms (e.g. MLflow, Weights & Biases)
Responsibilities
- Implement and optimize the latest machine learning algorithms/models to train multimodal foundation models on neural data
- Develop and maintain scalable, efficient, and reproducible machine-learning pipelines
- Conduct large-scale ML experiments, using the latest MLOps platforms
- Run large-scale distributed model training on high-performance computing clusters or cloud platforms
- Collaborate with machine learning researchers, data scientists, and systems engineers to ensure seamless integration of models and infrastructure
- Monitor and optimize model performance, resource utilization, and cost-effectiveness
Other
- Thorough knowledge of the principles of engineering and related natural sciences
- Demonstrated project management experience
- May require travel
- May be exposed to high voltage electricity, radiation or electromagnetic fields, lasers, noise > 80dB TWA, Allergens/Biohazards/Chemicals /Asbestos, confined spaces, working at heights ?10 feet, temperature extremes, heavy metals, unusual work hours or routine overtime and/or inclement weather
