Lila Sciences
(Senior) Scientist, Machine Learning (Active Learning & Bayesian ...
Lila Sciences - Cambridge, Massachusetts, us, 02140
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(Senior) Scientist, Machine Learning (Active Learning & Bayesian Optimization)
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Lila Sciences . Get AI-powered advice on this job and more exclusive features. Company Summary
Lila Sciences is a privately held, early-stage technology company pioneering the application of artificial intelligence to transform every aspect of the scientific method. Backed by Flagship Pioneering, we aim to realize ambitious results. Join our mission-driven team to contribute to the future of science. Our Physical Sciences effort focuses on developing novel AI and data-driven approaches to materials discovery and development, accelerating the transition to a sustainable economy. At Lila, we foster a cross-functional, collaborative environment that values inclusivity and diverse perspectives. We thrive in unstructured, creative settings where all voices are heard, recognizing that experience, transferable skills, and passion are vital. 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 enable data-driven materials discovery. Collaborate with the computational team to identify materials design pathways targeting desired properties and synthesis methods. Work with infrastructure and automation teams to transfer data and predictions in real time. Partner with the experimental team to drive material discovery and build domain-specific acquisition functions. Enhance scientific/technical expertise through literature review, conferences, and networking with opinion leaders. Communicate findings effectively through reports and presentations to stakeholders and leadership. Qualifications
Experience with uncertainty quantification, active learning, and Bayesian Optimization. Proficiency in implementing and tuning supervised models in Bayesian Optimization contexts (Gaussian processes, Bayesian Neural Networks) on various dataset sizes. Strong skills in at least one ML framework (PyTorch, TensorFlow, Jax) and the Python data science ecosystem (NumPy, SciPy, Pandas). Experience using cloud computing services to optimize training and evaluation of deep learning models. PhD in Computer Science, Applied Mathematics, or related fields with a focus on ML. Self-motivated, detail-oriented, with demonstrated industry or academic achievement. Excellent communication and presentation skills, capable of conveying complex technical information clearly. Enthusiastic about working in dynamic, fast-paced, entrepreneurial environments. Preferred Qualifications
Experience with AWS services. Experience integrating machine learning into experimental workflows. Additional Information
Flagship Pioneering is committed to equal employment opportunity and fosters an inclusive environment.
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(Senior) Scientist, Machine Learning (Active Learning & Bayesian Optimization)
role at
Lila Sciences . Get AI-powered advice on this job and more exclusive features. Company Summary
Lila Sciences is a privately held, early-stage technology company pioneering the application of artificial intelligence to transform every aspect of the scientific method. Backed by Flagship Pioneering, we aim to realize ambitious results. Join our mission-driven team to contribute to the future of science. Our Physical Sciences effort focuses on developing novel AI and data-driven approaches to materials discovery and development, accelerating the transition to a sustainable economy. At Lila, we foster a cross-functional, collaborative environment that values inclusivity and diverse perspectives. We thrive in unstructured, creative settings where all voices are heard, recognizing that experience, transferable skills, and passion are vital. 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 enable data-driven materials discovery. Collaborate with the computational team to identify materials design pathways targeting desired properties and synthesis methods. Work with infrastructure and automation teams to transfer data and predictions in real time. Partner with the experimental team to drive material discovery and build domain-specific acquisition functions. Enhance scientific/technical expertise through literature review, conferences, and networking with opinion leaders. Communicate findings effectively through reports and presentations to stakeholders and leadership. Qualifications
Experience with uncertainty quantification, active learning, and Bayesian Optimization. Proficiency in implementing and tuning supervised models in Bayesian Optimization contexts (Gaussian processes, Bayesian Neural Networks) on various dataset sizes. Strong skills in at least one ML framework (PyTorch, TensorFlow, Jax) and the Python data science ecosystem (NumPy, SciPy, Pandas). Experience using cloud computing services to optimize training and evaluation of deep learning models. PhD in Computer Science, Applied Mathematics, or related fields with a focus on ML. Self-motivated, detail-oriented, with demonstrated industry or academic achievement. Excellent communication and presentation skills, capable of conveying complex technical information clearly. Enthusiastic about working in dynamic, fast-paced, entrepreneurial environments. Preferred Qualifications
Experience with AWS services. Experience integrating machine learning into experimental workflows. Additional Information
Flagship Pioneering is committed to equal employment opportunity and fosters an inclusive environment.
#J-18808-Ljbffr