Mountain View, CA, 94039, USA
1 day ago
Phd Residency - ML & Optimization, Tapestry
Phd Residency - ML & Optimization, Tapestry Internship Mountain View, CA THE FOUNDATION OF AN AMAZING JOURNEY Our goal at X is to make the world a radically better place. In order to do that we seek fresh unexpected perspectives, from different fields, and that’s why we’re excited about you. Life here isn’t easy, but it’s fun. We’re trying to build things most people can’t even imagine, and we’re doing it with the hope of making a huge, positive impact on the world. You’ll be embedded into a moonshot project, where you’ll partner with team members to solve key challenges. This isn’t your ordinary internship. You’ll be positively challenged and pushed professionally, in ways that you may have never experienced. If this excites you - keep reading. DURING YOUR INTERNSHIP YOU CAN EXPECT: + To be placed on one of our confidential or public X projects + To get paid competitively and with Google benefits + To be part of a lively community of other Interns and Residents + Location: X's headquarters in Mountain View, CA + Start Date(s): January or February 2025 + Duration: a flexible 4 mo. to 1 year program based on project team needs and your availability ABOUT THE TEAM: Tapestry, isX’s moonshot for the electric grid. We are a team of software engineers, AI experts, researchers, power systems experts, and clean energy geeks who are impatient to tackle climate change head on through our energy system. Tapestry is on a mission to make the world’s electric grid visible so everyone, everywhere can access clean, affordable and reliable energy. We are working with partners around the world to create AI powered tools that will help everyone who works with the grid build and manage a carbon-free and secure electricity system. Currently incubating at X, Alphabet’s innovation lab, we are now building the team that will scale Tapestry for more global impact. We are looking for people who are passionate about revolutionizing the energy sector to join us at this critical growth stage. You’ll have the chance to be part of a rapidly growing team that has the agility and impact of an early stage company, while building on world-class technology created at Alphabet. Learn more aboutTapestry here. RESPONSIBILITIES: In this role, you’ll work closely with a team of passionate individuals to help build world class machine learning, computer vision, and optimization systems to revolutionize the power grid, energy production, distribution, integration of renewable energy sources towards a carbon neutral, sustainable world. You’ll be: + Learning the necessary domain knowledge to understand the project's challenges. + Generating ideas and hypotheses, prototyping solutions. + Engage in research collaborations and talks. + Leveraging your machine learning and optimization knowledge to tackle energy challenges. + Establishing and maintaining ML training pipelines. + Testing prototyped solutions for tackling challenging problems related to electricity grids. + Working closely with team members to implement solutions + Learning Moonshot thinking, attending and contributing to colloquium and tech talks. + Explore an exciting world, achieve great outcomes and have fun doing it! MINIMUM QUALIFICATIONS: + Currently enrolled in a Masters or PhD program in a STEM field, with a computational focus. + Solid Computer Science background or similar. + Strong background in Machine Learning (including Graph Neural Networks) and mathematical optimization. + Experience with one or more general purpose programming languages, including but not limited to: Python, C++. + Experience building (from scratch) and end-to-end training of Deep Learning models, working with large datasets. + Experience with machine learning systems, frameworks, algorithms or applications such as: computer vision, machine learning, information retrieval, data mining, time-series analysis, deep learning, optimization, signal processing. + Experience with at least one of modern machine learning frameworks, including but not limited to TensorFlow, PyTorch, Jax. + Curiosity, open mindedness, passion, growth mindset, teamwork, goal-oriented. PREFERRED QUALIFICATIONS: + Open-source projects that demonstrate relevant skills and/or publications in relevant conferences and journals (e.g. IEEE, NeurIPS, EPRI, ICML, ICLR, JMLR). + Great working knowledge of power systems. + Solid background in statistics. + Experience generating data to improve ML training. + Experience with MLOps tools/frameworks. Additional public information: About Tapestry: https://x.company/case-study/tapestry-cen/ https://x.company/blog/posts/tapestry-cen-planning/ About X: https://www.wired.com/video/watch/astro-teller-captain-of-moonshots-at-x-speaks-at-wired25 https://www.bloomberg.com/news/videos/2019-10-10/alphabet-x-s-astro-teller-on-bloomberg-studio-1-0-video The US base salary range for this position is $109,000 - $145,000 + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include benefits. At X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are 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. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please contact us at:x-accommodation-request@x.team.
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