Myzeus
Zeus AI Machine Learning Scientist (Atmospheric Science) Cambridge, MARemoteFull time Company website Apply for Machine Learning Scientist (Atmospheric Science)
At Zeus AI, we're building an AI platform for Earth observations, supported by NASA, the Department of Energy (DOE), and the Department of Defense (DOD). Our interdisciplinary team of engineers and scientists is dedicated to a mission: to create a large-scale foundation model that will transform data assimilation, weather forecasting, and diverse scientific and commercial applications. Advised by industry-leading experts, our core objective is to enhance our understanding and management of the planet through research. We are a remote-first company offering in-person work in Cambridge for team members located nearby. About Zeus AI
Zeus AI integrates multi-modal, multi-resolution data to create a unified model of our planet. Our model predicts timely, complete, high-resolution global information. Our technology, developed at NASA Ames Research Center in Silicon Valley, uses state of the art computer vision to produce accurate, real-time and high-resolution weather variables. Our team has numerous publications and awards at the intersection of machine learning and Earth sciences with experience working in government agencies, academia, and private industry. Description
Location
Cambride, MA or remote The challenge
Zeus AI is building foundation models for global and regional scale Earth system modeling, ingesting observations to produce an accurate low latency representation of the planet through machine learning data assimilation. This problem is often framed as observation to observation forecasting. We are tackling this problem with a multi-modal and multi-resolution modeling framework using numerous observation types including satellites, stations, aircraft, drones, and marine vessels to power forecasts and digital twin models. The machine learning architecture plays a key role in this system to learn relationships across sensors best characterizing the systems state. The role
We're looking for a Machine Learning Scientist to join our core science and engineering team. In this role, you'll be instrumental in building foundation models for data assimilation and forecasting. You'll develop and fine-tune generative machine learning models for medium-range forecasting, working large-scale, heterogeneous Earth system datasets, rigorously evaluating model performance, and scaling training runs on large clusters. You'll apply these models to critical applications like weather prediction, aviation planning, air quality, renewable energy, and extreme weather. Duties and responsibilities
Advance the development of medium-range forecasting using EarthNet, Zeus AIs foundation model for Earth observations Explore and adapt cutting-edge machine learning architectures, including Graph Neural Networks (GNNs), diffusion models, and autoregressive models for forecasting Carry out large-scale training runs in high performance computing environments Design and train data-specific compression algorithms to optimize the representation of heterogeneous Earth observation data for large foundation models Lead efforts to fine-tune models for downstream tasks like forecasting, air quality, atmospheric convection, and localized predictions, potentially utilizing super-resolution techniques or specialized architectures Collaborate closely with data engineers and domain scientists to integrate diverse Earth observation datasets into model training and validation pipelines. Engage in scientific communication including publications and conference presentations Qualifications
Ph.D degree in computer science, physics, mathematics, or related quantitative field 2+ years of industry experience as a software or machine learning engineer 2+ years of experience working with Earth science datasets including remote sensing datasets, weather forecasts, and/or climate simulations Excellent programming skills in Python including proficiency with deep learning frameworks like PyTorch or Tensorflow Experience with best practices in scientific software development, including version control and code optimization for high performance computing environments Proven experience implementing and training generative machine learning models including but not limited to variational autoencoders, transformers, and GNNs Strong interest in scientific research and development, with a desire to contribute to cutting-edge advancements Strong communication and collaboration skills to work effectively with an interdisciplinary team What we offer
The opportunity to work on cutting-edge research at the intersection of Earth science and machine learning, with access to extensive computing resources. A collaborative and stimulating work environment alongside a team of passionate and talented scientists and engineers. Competitive compensation, including salary, equity, and a comprehensive benefits package (health, dental, vision, and 401k retirement options). Flexible work arrangements and a co-working space benefit. The chance to make a tangible difference in the world by contributing to climate solutions and significantly improving weather forecasting.
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At Zeus AI, we're building an AI platform for Earth observations, supported by NASA, the Department of Energy (DOE), and the Department of Defense (DOD). Our interdisciplinary team of engineers and scientists is dedicated to a mission: to create a large-scale foundation model that will transform data assimilation, weather forecasting, and diverse scientific and commercial applications. Advised by industry-leading experts, our core objective is to enhance our understanding and management of the planet through research. We are a remote-first company offering in-person work in Cambridge for team members located nearby. About Zeus AI
Zeus AI integrates multi-modal, multi-resolution data to create a unified model of our planet. Our model predicts timely, complete, high-resolution global information. Our technology, developed at NASA Ames Research Center in Silicon Valley, uses state of the art computer vision to produce accurate, real-time and high-resolution weather variables. Our team has numerous publications and awards at the intersection of machine learning and Earth sciences with experience working in government agencies, academia, and private industry. Description
Location
Cambride, MA or remote The challenge
Zeus AI is building foundation models for global and regional scale Earth system modeling, ingesting observations to produce an accurate low latency representation of the planet through machine learning data assimilation. This problem is often framed as observation to observation forecasting. We are tackling this problem with a multi-modal and multi-resolution modeling framework using numerous observation types including satellites, stations, aircraft, drones, and marine vessels to power forecasts and digital twin models. The machine learning architecture plays a key role in this system to learn relationships across sensors best characterizing the systems state. The role
We're looking for a Machine Learning Scientist to join our core science and engineering team. In this role, you'll be instrumental in building foundation models for data assimilation and forecasting. You'll develop and fine-tune generative machine learning models for medium-range forecasting, working large-scale, heterogeneous Earth system datasets, rigorously evaluating model performance, and scaling training runs on large clusters. You'll apply these models to critical applications like weather prediction, aviation planning, air quality, renewable energy, and extreme weather. Duties and responsibilities
Advance the development of medium-range forecasting using EarthNet, Zeus AIs foundation model for Earth observations Explore and adapt cutting-edge machine learning architectures, including Graph Neural Networks (GNNs), diffusion models, and autoregressive models for forecasting Carry out large-scale training runs in high performance computing environments Design and train data-specific compression algorithms to optimize the representation of heterogeneous Earth observation data for large foundation models Lead efforts to fine-tune models for downstream tasks like forecasting, air quality, atmospheric convection, and localized predictions, potentially utilizing super-resolution techniques or specialized architectures Collaborate closely with data engineers and domain scientists to integrate diverse Earth observation datasets into model training and validation pipelines. Engage in scientific communication including publications and conference presentations Qualifications
Ph.D degree in computer science, physics, mathematics, or related quantitative field 2+ years of industry experience as a software or machine learning engineer 2+ years of experience working with Earth science datasets including remote sensing datasets, weather forecasts, and/or climate simulations Excellent programming skills in Python including proficiency with deep learning frameworks like PyTorch or Tensorflow Experience with best practices in scientific software development, including version control and code optimization for high performance computing environments Proven experience implementing and training generative machine learning models including but not limited to variational autoencoders, transformers, and GNNs Strong interest in scientific research and development, with a desire to contribute to cutting-edge advancements Strong communication and collaboration skills to work effectively with an interdisciplinary team What we offer
The opportunity to work on cutting-edge research at the intersection of Earth science and machine learning, with access to extensive computing resources. A collaborative and stimulating work environment alongside a team of passionate and talented scientists and engineers. Competitive compensation, including salary, equity, and a comprehensive benefits package (health, dental, vision, and 401k retirement options). Flexible work arrangements and a co-working space benefit. The chance to make a tangible difference in the world by contributing to climate solutions and significantly improving weather forecasting.
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