Machine Learning Scientist, Machine Learning (Active Learning & Bayesian Optimization)
Onsite in Cambridge, Massachusetts, USA
We are seeking a talented (Senior) Machine Learning Scientist with expertise in Active Learning and Bayesian Optimization, to join our innovative team in Cambridge, Massachusetts.
Key Responsibilities:
- Design, build, and scale supervised ML models for Active Learning and Bayesian optimization in materials synthesis and performance.
- Implement best practices and innovate methods for uncertainty quantification.
- Integrate datasets of multiple fidelities and sources for data-driven materials discovery.
- Collaborate with the computational team to identify materials design pathways and their synthesis.
- Work with infrastructure and automation teams for real-time data and prediction transfers.
- Drive material discovery and development with the experimental team, building domain-specific acquisition functions.
- Stay updated on ML research through literature reviews, conferences, and networking.
- Report findings to stakeholders and leadership through written reports and presentations.
Qualifications:
- Experience with uncertainty quantification, active learning, and Bayesian optimization.
- Skilled in implementing, evaluating, and tuning supervised models in a Bayesian optimization context.
- Proficiency in ML frameworks (PyTorch/TensorFlow/Jax) and the Python data science ecosystem (Numpy, SciPy, Pandas, etc.).
- Experience with cloud computing services for training and evaluating models.
- PhD in Computer Science, Applied Mathematics, or a related field with a focus on ML.
- Independent thinker with strong attention to detail.
- Demonstrated industry experience or notable academic achievement.
- Excellent communication and presentation skills.
- Enthusiastic about working in a fast-paced, entrepreneurial, and technical setting.
Preferred Qualifications:
- Experience using AWS services.
- Experience with integrating machine learning in experimental workflows.
Please apply for more information, we look forward to hearing from you
