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(Senior) Scientist, Machine Learning (Active Learning & Bayesian Optimization)

Lila Sciences

Salary not specified
Aug 5, 2025
Cambridge, MA, US
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The company is looking to accelerate the transition to a sustainable economy by developing a novel AI and data-driven approach to materials discovery and development.

Requirements

  • Experience with uncertainty quantification, active learning and Bayesian Optimization
  • Experience implementing, evaluating, and hyperparameter tuning small and large supervised models in a Bayesian Optimization context (Gaussian processes, Bayesian Neural Networks) on small and large datasets
  • Strong experience in at least one ML framework (PyTorch/TensorFlow/Jax) and robust experience in Python data science ecosystem (Numpy, SciPy, Pandas, etc.)
  • Experience using a cloud computing service to reduce runtime to train and evaluate deep learning models
  • Experience using AWS services
  • Experience with machine learning integration in experiment workflows

Responsibilities

  • Design, build and scale supervised ML models for active learning and Bayesian Optimization of materials synthesis and performance
  • Implement best practices and innovate methods for uncertainty quantification
  • Combine datasets of multiple fidelities and sources to power data-driven materials discovery
  • Work with the computational team to identify materials design pathways that target desired functional properties and their synthesis
  • Work with infrastructure and automation teams to transfer data and predictions in real time
  • Work with the experimental team to drive material discovery and development, and build domain-specific acquisition functions
  • Continually cultivate scientific/technical expertise through critical review of ML literature, attending conferences, and developing relationships with key opinion leaders

Other

  • Strong self-starter and independent thinker, with strong attention to detail
  • Demonstrated industry experience or academic achievement
  • Excellent communication and presentation skills, capable of conveying technical information in a clear and thorough manner
  • Eager to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting
  • PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field