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Scientific Machine Learning Jobs in Colorado (NOW HIRING)

The Marlin Alliance

Sr Machine Learning Engineer

Denver, CO

$107K - $147K/yr

Senior Machine Learning Engineer The Marlin Alliance, Inc. is seeking a talented and experienced ... Collaborate with engineers, data scientists, and mission stakeholders to align ML solutions with ...

Ibotta

Principal Machine Learning Engineer

Denver, CO · On-site +1

$228K - $253K/yr

Ibotta is seeking a Principal Machine Learning Engineer to join our Core Data & Analytics team and ... Mentor ML Engineers and Data Scientists, fostering a culture of technical ownership, rigorous ...

AppFolio

CO

D. in Computer Science, Machine Learning, or a related field (required). * 10+ years of experience building software systems, with significant focus on ML/AI (or equivalent impact). * Combined ...

Ibotta

$228K - $253K/yr

Ibotta is seeking a Principal Machine Learning Engineer to join our Core Data & Analytics team and ... Mentor ML Engineers and Data Scientists, fostering a culture of technical ownership, rigorous ...

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Scientific Machine Learning information

Is ML a high paying job?

Scientific Machine Learning roles typically offer high salaries due to the specialized skills required, such as expertise in deep learning, data analysis, and programming with tools like Python and TensorFlow. Compensation varies by industry, experience, and location but generally exceeds average tech salaries for comparable roles.

Which 3 jobs will survive AI?

Scientific Machine Learning professionals will likely continue to be in demand due to their expertise in developing and applying AI models to complex scientific problems. Roles such as data scientists, AI researchers, and machine learning engineers are expected to persist because they require specialized knowledge, critical thinking, and ongoing innovation that AI tools complement rather than replace. These jobs often involve interdisciplinary skills, programming, and understanding of domain-specific data, making them more resilient to automation.

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 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.

How much does a machine learning scientist make?

A machine learning scientist typically earns between $90,000 and $150,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in deep learning or natural language processing can earn higher salaries, often exceeding $180,000.

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 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.

Is 40 too late for data science?

Scientific Machine Learning roles often value skills and experience over age, and many professionals transition into data science or machine learning at various stages of their careers. Learning relevant tools like Python, TensorFlow, or scikit-learn and gaining practical experience can help regardless of age, making 40 not too late to pursue this field.
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:
What job categories do people searching Scientific Machine Learning jobs in Colorado look for? The top searched job categories for Scientific Machine Learning jobs in Colorado are:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Colorado as of June 2026, with employment types broken down into 78% Full Time, and 22% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.

Senior Machine Learning Engineer

True Anomaly

Denver, CO

$155K - $260K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Posted yesterday

Job description

Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.
OUR MISSION
True Anomaly delivers decisive capabilities for space superiority. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors - enabling the U.S. and its Allies to secure the space environment and counter threats from the ultimate high ground.
OUR VALUES

  • Be the offset. We create asymmetric advantages with creativity and ingenuity.
  • What would it take? We challenge assumptions to deliver ambitious results.
  • It's the people. Our team is our competitive advantage and we are better together.
YOUR MISSION
As a member of the Applied Algorithms and Autonomy team, you will design, build, and deploy core machine learning and AI capabilities for True Anomaly. You will work with a talented cross-functional team to advance technology at the intersection of artificial intelligence, machine learning, and data-driven decision-making. This will involve hands-on development across areas including object classification and discrimination, anomaly detection, and threat assessment. You are a first-principles engineer who takes ownership of the systems you build and delivers results.
RESPONSIBILITIES
  • Design, implement, and test ML/AI models that support threat assessment, object discrimination, and decision-making in operationally relevant environments
  • Own the full ML development lifecycle - from data ingestion and feature engineering through model training, evaluation, and production deployment
  • Collaborate with cross-functional teams to translate operational requirements into robust, production-ready ML capabilities
  • Establish and maintain rigorous model evaluation practices to ensure reliability and performance in real-world conditions
  • Write clean, well-documented, and testable code in support of AI/ML capabilities
QUALIFICATIONS
  • Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a similar discipline
  • Proficient in Python
  • Solid understanding of statistics, probability, and optimization
  • Experience with ML frameworks such as PyTorch, TensorFlow, or JAX
  • 4+ years of experience designing, training, and deploying ML models in real-world systems
  • Demonstrated ability to work in a multidisciplinary team and solve complex problems from first principles
  • Passion for spaceflight and advancing capabilities related to space domain awareness and space security
PREFERRED SKILLS AND EXPERIENCE
  • Master's or PhD in machine learning, computer science, data science, or a related discipline
  • Strong background in one of the following core ML disciplines:
    • Anomaly & outlier detection: statistical, density-based, and deep learning approaches
    • Object discrimination: multi-class and fine-grained classification, metric learning, few-shot learning, evidential reasoning and Dempster-Shafer Theory (DST) for belief combination and conflict resolution under uncertain or incomplete sensor data
    • Unsupervised learning: clustering, dimensionality reduction, generative modeling
    • Sequential and temporal modeling: time-series analysis and sequential modeling
  • Experience deploying models to edge or resource-constrained environments with real-time processing requirements
  • Familiarity with space domain data such as space object catalog data, observational data, or RSO characterization
  • Experience with MLOps tooling: experiment tracking (MLflow, W&B), model versioning, CI/CD for ML pipelines
  • Background in model interpretability, uncertainty quantification, or safety-critical ML validation
COMPENSATION
  • Base Salary: $155,000 - $260,000
  • Equity + Benefits including Health, Dental, Vision, HRA/HSA options, PTO and paid holidays, 401K, Parental Leave
Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, location, and experience.
ADDITIONAL REQUIREMENTS
  • Work Location- this is a fully onsite role. Candidates must be based in or able to commute to our Denver or Long Beach office daily.
  • Work environment-the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job.
  • Physical demands-the physical demands of the job, including bending, sitting, lifting and driving.

This position will be open until it is successfully filled. To submit your application, please follow the directions below. #LI-Onsite
To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State.
True Anomaly is committed to equal employment opportunity on any basis protected by applicable state and federal laws. If you have a disability or additional need that requires accommodation, please do not hesitate to let us.

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