Preferred : • PhD in a technical field such as computer science, biology, climate, astrophysics ... machine-learning hosting software frameworks, such as NVIDIA Dynamo, TensorFlow Serving, Ray, etc ...
Preferred : • PhD in a technical field such as computer science, biology, climate, astrophysics ... machine-learning hosting software frameworks, such as NVIDIA Dynamo, TensorFlow Serving, Ray, etc ...
Preferred : • PhD in a technical field such as computer science, biology, climate, astrophysics ... machine-learning hosting software frameworks, such as NVIDIA Dynamo, TensorFlow Serving, Ray, etc ...
Preferred : • PhD in a technical field such as computer science, biology, climate, astrophysics ... machine-learning hosting software frameworks, such as NVIDIA Dynamo, TensorFlow Serving, Ray, etc ...
Data Platform Engineer
New York, NY · On-site
$125K - $150K/yr
We run Apache Airflow 3 on Astronomer with pipelines that process terabytes of real-world physical ... We use Python for our pipeline environment, machine learning, and developer tooling; we don't ...
Data Platform Engineer
New York, NY · On-site
$125K - $150K/yr
We run Apache Airflow 3 on Astronomer with pipelines that process terabytes of real-world physical ... We use Python for our pipeline environment, machine learning, and developer tooling; we don't ...
... machine learning are combined in computational approaches. In addition to ICC, the Flatiron Institute hosts the Centers for Computational Astrophysics, Computational Biology, Computational ...
... machine learning are combined in computational approaches. In addition to ICC, the Flatiron Institute hosts the Centers for Computational Astrophysics, Computational Biology, Computational ...
... machine learning applications. * The Analytics Architecture vertical within the Analytics team is ... Experience working with pipeline scheduling tools such as Airflow & Astronomer. * Experience ...
... machine learning applications. * The Analytics Architecture vertical within the Analytics team is ... Experience working with pipeline scheduling tools such as Airflow & Astronomer. * Experience ...
Staff Data Infrastructure Engineer
New York, NY · On-site
$212K - $265K/yr
... and Machine Learning teams. This is not an analytics role. Who you are A technical leader who ... Experience maintaining and scaling pipeline orchestration infrastructure (Airflow/Astronomer ...
Staff Data Infrastructure Engineer
New York, NY · On-site
$212K - $265K/yr
... and Machine Learning teams. This is not an analytics role. Who you are A technical leader who ... Experience maintaining and scaling pipeline orchestration infrastructure (Airflow/Astronomer ...
Senior Analytics Engineer
Manhattan, NY · On-site
... machine learning applications. * The Analytics Architecture vertical within the Analytics team is ... Experience working with pipeline scheduling tools such as Airflow & Astronomer. * Experience ...
Senior Analytics Engineer
Manhattan, NY · On-site
... machine learning applications. * The Analytics Architecture vertical within the Analytics team is ... Experience working with pipeline scheduling tools such as Airflow & Astronomer. * Experience ...
Machine Learning Astronomy information
What is the difference between Machine Learning Astronomy vs Data Scientist?
| Aspect | Machine Learning Astronomy | Data Scientist |
|---|---|---|
| Required Credentials | Degree in Astronomy, Physics, or related fields; knowledge of machine learning | Degree in Computer Science, Statistics, or related fields; strong programming skills |
| Work Environment | Research institutions, observatories, academia | Corporate, tech companies, consulting firms |
| Industry Usage | Analyzing astronomical data, developing models for celestial phenomena | Business analytics, predictive modeling, data visualization |
Machine Learning Astronomy focuses on applying machine learning techniques to astronomical data within research settings, while Data Scientists work across various industries analyzing data to inform business decisions. Both roles require strong analytical skills and programming knowledge but differ in domain focus and work environment.
What is machine learning astronomy?
Machine learning astronomy is the application of machine learning techniques to analyze and interpret astronomical data. This field combines computer science, statistics, and astronomy to automate tasks such as classifying celestial objects, detecting anomalies, and predicting astronomical events. With the increasing volume of data from telescopes and space missions, machine learning helps astronomers process and extract meaningful insights more efficiently. Researchers in this area develop algorithms that can learn patterns from vast datasets, leading to new discoveries and a deeper understanding of the universe.
What are the key skills and qualifications needed to thrive as a machine learning astronomer, and why are they important?
To thrive as a Machine Learning Astronomer, you need a strong background in astrophysics, statistical analysis, and programming (often with a PhD in a related field). Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and astronomical data systems is essential. Critical thinking, problem-solving, and effective collaboration are key soft skills for innovating solutions and working within research teams. These skills enable the effective analysis of large astronomical datasets, driving new discoveries and advancements in the field.
What are some common challenges faced by professionals working in machine learning astronomy?
Machine learning astronomers often encounter challenges such as handling extremely large and complex datasets, ensuring data quality, and effectively preprocessing astronomical data to reduce noise and artifacts. Additionally, interpreting model results in a scientific context can be demanding, as it requires both technical expertise and domain knowledge. Collaboration with astronomers, data engineers, and software developers is essential to ensure that machine learning models are both accurate and scientifically meaningful.
What are popular job titles related to Machine Learning Astronomy jobs in New York? For Machine Learning Astronomy jobs in New York, the most frequently searched job titles are:
What job categories do people searching Machine Learning Astronomy jobs in New York look for? The top searched job categories for Machine Learning Astronomy jobs in New York are:
What cities in New York are hiring for Machine Learning Astronomy jobs? Cities in New York with the most Machine Learning Astronomy job openings:
Full-time
Re-posted 25 days ago
Job description
Job Summary:
Schmidt Sciences is a nonprofit organization focused on accelerating scientific knowledge and breakthroughs. The Solutions Architect will serve as a strategic liaison for researchers, assessing their GPU and AI/ML needs while leading the technical onboarding process to enhance scientific discovery through advanced computing resources.
Responsibilities:
• Serve as a technical and strategic advisor to Schmidt leadership and principal investigators across a diverse portfolio of scientific disciplines.
• Conduct deep-dive technical consultations with academic labs and other research organizations to translate scientific goals into specific GPU, AI/ML, and HPC and cloud requirements.
• Lead the end-to-end onboarding of research teams and grantee institutions onto the Schmidt Sciences compute infrastructure, and provide comprehensive training to boost cloud and compute proficiency of our users.
• Act as a "Proposal Concierge," helping grantees align their research proposals and research activities with the available compute resources to maximize scientific impact.
• Deliver critical field insights from researchers to internal product and engineering teams to drive the continuous improvement of compute services and research tools.
Qualifications:
Required:
• A post-graduate degree in a technical field such as computer science, biology, climate, astrophysics, or a related computational science
• 8+ years of combined experience spanning AI/ML infrastructure and research-facing technical advisory; experience building networks across academic, nonprofit, or government research is strongly preferred
• Exceptional ability to communicate complex technical and scientific concepts to both high-impact research scientists and executive leadership.
• Hands-on experience with cluster workload management using Slurm and Kubernetes.
• Successful track record of accelerating time from grant approval to first result for computationally intensive projects.
• History of collaborative impact in high-intensity, team-based environments.
• Sense of controlled urgency in driving work to completion.
• The highest integrity and ability to maintain confidentiality.
• Be able to travel within the U.S. on a regular basis as needed.
• Understanding of the tech stack needed to design, train, deploy, and maintain state-of-the-art AI models at a production scale.
• Experience producing technical writing for expert and general audiences.
Preferred:
• PhD in a technical field such as computer science, biology, climate, astrophysics, or a related computational science
• Proven track record of supporting large-scale, federally funded, or private scientific grant proposals.
• In-depth knowledge of data center storage and networking technologies and solutions.
• Proficiency with modern machine-learning hosting software frameworks, such as NVIDIA Dynamo, TensorFlow Serving, Ray, etc.
• Prior leadership of data center infrastructure initiatives and projects, such as evaluating hardware scalability, securing data, or executing large-scale upgrades.
• Expertise in relevant technical focus areas, e.g., AI model performance monitoring or network and storage optimization, etc.
• Expert-level experience and industry credentials in the software and hardware frameworks that drive modern AI, and competence in at least one, and preferably multiple, fields of science impacted by modern AI.
• Ability to work with and effectively translate technical concepts across multiple scientific disciplines.
• Ability to critically evaluate scientific and technical publications and emerging methods in related disciplines.
• Experience working with science-focused institutions such as philanthropic organizations or academic/government research institutions.
Company:
Schmidt Sciences dedicated to advancing science and technology for positive global impact. Founded in 2024, the company is headquartered in , , with a team of 11-50 employees. The company is currently Early Stage.
Schmidt Sciences is a nonprofit organization focused on accelerating scientific knowledge and breakthroughs. The Solutions Architect will serve as a strategic liaison for researchers, assessing their GPU and AI/ML needs while leading the technical onboarding process to enhance scientific discovery through advanced computing resources.
Responsibilities:
• Serve as a technical and strategic advisor to Schmidt leadership and principal investigators across a diverse portfolio of scientific disciplines.
• Conduct deep-dive technical consultations with academic labs and other research organizations to translate scientific goals into specific GPU, AI/ML, and HPC and cloud requirements.
• Lead the end-to-end onboarding of research teams and grantee institutions onto the Schmidt Sciences compute infrastructure, and provide comprehensive training to boost cloud and compute proficiency of our users.
• Act as a "Proposal Concierge," helping grantees align their research proposals and research activities with the available compute resources to maximize scientific impact.
• Deliver critical field insights from researchers to internal product and engineering teams to drive the continuous improvement of compute services and research tools.
Qualifications:
Required:
• A post-graduate degree in a technical field such as computer science, biology, climate, astrophysics, or a related computational science
• 8+ years of combined experience spanning AI/ML infrastructure and research-facing technical advisory; experience building networks across academic, nonprofit, or government research is strongly preferred
• Exceptional ability to communicate complex technical and scientific concepts to both high-impact research scientists and executive leadership.
• Hands-on experience with cluster workload management using Slurm and Kubernetes.
• Successful track record of accelerating time from grant approval to first result for computationally intensive projects.
• History of collaborative impact in high-intensity, team-based environments.
• Sense of controlled urgency in driving work to completion.
• The highest integrity and ability to maintain confidentiality.
• Be able to travel within the U.S. on a regular basis as needed.
• Understanding of the tech stack needed to design, train, deploy, and maintain state-of-the-art AI models at a production scale.
• Experience producing technical writing for expert and general audiences.
Preferred:
• PhD in a technical field such as computer science, biology, climate, astrophysics, or a related computational science
• Proven track record of supporting large-scale, federally funded, or private scientific grant proposals.
• In-depth knowledge of data center storage and networking technologies and solutions.
• Proficiency with modern machine-learning hosting software frameworks, such as NVIDIA Dynamo, TensorFlow Serving, Ray, etc.
• Prior leadership of data center infrastructure initiatives and projects, such as evaluating hardware scalability, securing data, or executing large-scale upgrades.
• Expertise in relevant technical focus areas, e.g., AI model performance monitoring or network and storage optimization, etc.
• Expert-level experience and industry credentials in the software and hardware frameworks that drive modern AI, and competence in at least one, and preferably multiple, fields of science impacted by modern AI.
• Ability to work with and effectively translate technical concepts across multiple scientific disciplines.
• Ability to critically evaluate scientific and technical publications and emerging methods in related disciplines.
• Experience working with science-focused institutions such as philanthropic organizations or academic/government research institutions.
Company:
Schmidt Sciences dedicated to advancing science and technology for positive global impact. Founded in 2024, the company is headquartered in , , with a team of 11-50 employees. The company is currently Early Stage.