Staff Engineer, Machine Learning

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We believe innovation and growth are driven by an inclusive culture and a diverse workforce. We’re dedicated to empowering people to be their true selves. Together, we’re building a better tomorrow for our employees, customers, partners, and communities.

Our team is at the forefront of applying machine learning to real-world organic material research. As part of a dynamic group of researchers, engineers, you’ll collaborate on innovative projects that transform data into insights and breakthrough discoveries of new material. As a Senior Machine Learning engineer in the Display Lab, you'll immerse yourself in an environment that values creativity, collaboration, and cutting-edge experimentation—working alongside experts who are as passionate about technology and science as you are.

Location: Hybrid, working onsite at our San Jose office/headquarters 3-5 days per week., with the flexibility to work remotely the remainder of your time

What You’ll Do

• Collaborate with a multidisciplinary team of researchers and engineers to drive advancements in machine learning applications for organic material research.
• Participate in cutting-edge research by designing, developing, and validating innovative ML models tailored to molecule property prediction and generation.
• Develop and refine simulation platforms by integrating state-of-the-art tools and methodologies, ensuring robustness and scalability of research outputs.
• Integrate advanced data analytics frameworks to manage, preprocess, and interpret experimental data, enabling actionable insights for material innovation.
• Engage with academic communities toward paper publications.

  What You Bring

• BS Computer Science or equivalent with a minimum of 10+ years or a MS in Computer Science with 8+ years or PhD 5+ years Computer Science, Computational Chemistry, Material Science preferred.
• Experience in DFT,MD simulation tools such as ORCA, Q-Chem, GROMACS
• Experience in calculating optoelectronic properties of OLED molecules is a plus
• Experience in deep learning. Specific machine learning skills in GNN, reinforcement learning is a plus.
• Experience in adapting standard machine learning methods (e.g. distributed clusters, and GPU clusters)
• A willingness to dive deep, experiment rapidly and get things done

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