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Scientific Machine Learning Jobs in Colorado (NOW HIRING)

Experience developing production-quality scientific or machine learning software. * Familiarity with modern architectures used in weather and geoscience ML, including graph neural networks and ...

Experience developing production-quality scientific or machine learning software. * Familiarity with modern architectures used in weather and geoscience ML, including graph neural networks and ...

Machine Learning Engineer

Aurora, CO · On-site

$77K - $176K/yr

Experience with data science or machine learning * Knowledge of python, java, or c++ programming * TS/SCI clearance * Bachelor's degree Nice If You Have: * Experience with machine learning operations ...

Machine Learning Engineer LOCATION Aurora, CO 80014 CLEARANCE TS/SCI Full Poly (Please note this ... You will collaborate with data scientists, engineers, and product teams to turn data into ...

Oversee teams of data scientists, modelers, and ML engineers to deliver innovative and scalable ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

Machine Learning Engineer

Aurora, CO · On-site

$77K - $176K/yr

Experience with data science or machine learning * Knowledge of python, java, or c++ programming * TS/SCI clearance * Bachelor's degree Nice If You Have: * Experience with machine learning operations ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Colorado?

For Scientific Machine Learning jobs in Colorado, the most frequently searched job titles are:

Infographic showing various Scientific Machine Learning job openings in Colorado as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Scientist

Golden, CO • On-site

Socket.dev
Network Security • 1 - 10 employees

Other

Medical, PTO

Posted 14 days ago


Job description

The R&D Team at Tomorrow.io is a dynamic mix of scientists and engineers. Our mission is to generate the best and most novel data and models across all times: historical, real-time, and forecast. The story just begins when the data hits our ingest and post-processing services. Every product that the user sees is the result of a pipeline of algorithms that needs to be run quickly and continuously, in an operational environment. We are the team that builds the architecture behind the data and the models, preparing the weather analyses for the Product and Engineering team to serve the masses.

We are seeking a Machine Learning Scientist with experience in AI weather prediction to help advance Tomorrow.io’s next-generation forecasting capabilities. In this role, you will combine atmospheric science expertise with state-of-the-art machine learning methods to improve forecast skill, including developing new ways to leverage observations from Tomorrow.io’s microwave sounder constellation. You will work collaboratively from proof-of-concept through deployment, with a focus on translating research advances into operational, customer-impacting products.

What you’ll do:
  • Conduct research and development at the intersection of machine learning and weather prediction.
  • Develop, train, evaluate, and improve AI-based weather prediction models, with a focus on measurable improvements in forecast skill and customer value.
  • Develop approaches to maximize the value of observations from Tomorrow.io’s satellite constellation for weather prediction.
  • Explore machine learning approaches for incorporating observations into forecast systems, including ML-based data assimilation and related methods.
  • Work with large atmospheric and geophysical datasets and build reproducible, maintainable ML workflows.
  • Collaborate with scientists and engineers to transition successful research from proof-of-concept into scalable, operational systems.
  • Communicate results clearly through internal reviews, technical discussions, and, where appropriate, conferences and peer-reviewed publications.
What you bring:
  • Graduate degree in atmospheric science, meteorology, computer science, or a related quantitative field.
  • 2+ years of experience developing machine learning approaches for weather prediction or closely related geoscience problems. Relevant PhD research developing ML models may count toward this experience.
  • Hands-on experience training, evaluating, and working with deep learning models for atmospheric science.
  • Strong understanding of ML development best practices specific to atmospheric science, including experimental design, model evaluation, testing, documentation, and code review.
  • Strong written and verbal communication skills and the ability to explain complex technical results to both technical and non-technical audiences. Demonstrated ability to collaborate across disciplines and deliver high-quality work.
  • Experience conducting independent research, demonstrated through publications, research leadership, open-source contributions, or other technical work.
Nice to have:
  • Knowledge of or experience with data assimilation, including traditional and/or machine-learning-based approaches, is a strong plus.
  • Knowledge of satellite remote sensing and experience working with satellite observations.
  • Experience developing production-quality scientific or machine learning software.
  • Familiarity with modern architectures used in weather and geoscience ML, including graph neural networks and transformers.
  • AI-first mentality towards research and development (e.g., using AI-assisted development tools)

So, if you're looking to join a team that is not only at the forefront of innovation but also working towards building the biggest weather platform in the world, this is the place for you! If your experience is close but only fulfills some requirements,

Tomorrow.io is on a mission to build a special company. We are focused on hiring people with different backgrounds, perspectives, and experiences to achieve our goal.

Tomorrow.io is proud to be an Equal Employment Opportunity and Af irmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Tomorrow.io participates in the E-Verify program in all US states, as required by law.

At tomorrow.io we have established a workplace culture that values fairness and equal opportunities and we believe it is crucial for fostering a positive and productive environment. Regularly reviewing and adjusting pay practices to align with legitimate drivers of pay, such as job level, geographic location, and performance, demonstrates a commitment to maintaining equity within the organization.This commitment to ongoing assessment and improvement is key to creating a workplace that is not only diverse and inclusive but also fair and just. The anticipated salary range for this role is $145k-$160k subject to local market and candidates skills and experience. Comprehensive health benefits, unlimited paid time off and other benefits included. Relocation assistance may be offered/available for certain roles.

Tomorrow.io is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at jobs@tomorrow.io

About Tomorrow.io:

Tomorrow.io is helping Countries, Businesses and Individuals better manage their Climate Security Challenges. Fully customizable to any industry impacted by the weather, customers around the world including Uber, Delta, Ford, National Grid and more use Tomorrow.io to dramatically improve operational efficiency. Tomorrow.io was built from the ground up to help teams predict the business impact of weather, streamline team communication and action plans, improve productivity, and optimize profit margins.

Space:

In case you have not heard, we are also going to space with our Operation Tomorrow Space initiative. We are building the first-of-its-kind proprietary satellites equipped with radar, and launching them into space to improve weather forecasting technology for everyone on Earth.

Ethos:

Our ethos guides us in everything we do - The people of Tomorrow are here to make an impact, they show true grit, and always put people first.

How we roll:

We believe that magic happens when people work together. The People of Tomorrow take ownership with a bias for action. We believe in transparency and directness, putting work before ego, and empathy. The People of Tomorrow have a can-do attitude, are resilient, and curious. They are growth oriented, value people striving to be experts, and love to have fun. Here, your success is achieved by your impact and deliveries and not by the hours you put in. We have flexible hours and unlimited vacation days policy. The People of Tomorrow show empathy, mutual respect and work as one diverse team. We grow fast and move faster but we always see people first. Each person has their own career growth path for we believe that the only way for the company to grow is if you grow.

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