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

🚀 About Lila Sciences

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai    

At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, please apply.

🛠️ What You'll Be Building

• 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
• Report findings to stakeholders and leadership in written reports and verbal presentations.

🧰 What You’ll Need to Succeed

• 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
• PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field
• 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

✨ Bonus Points For

• Experience using AWS services
• Experience with machine learning integration in experiment workflows

🌈 We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

🤝 A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.