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Scientific Machine Learning Jobs in Seattle, WA (NOW HIRING)

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

Collaborate with data scientists and flight software engineers to integrate AI capabilities into ... Proven experience deploying machine learning models into production. * Strong software engineering ...

Machine Learning Engineer

Seattle, WA · On-site

$95 - $135/hr

Collaborate with data scientists and flight software engineers to integrate AI capabilities into ... Proven experience deploying machine learning models into production. * Strong software engineering ...

Senior Machine Learning Scientist

Seattle, WA · On-site

$104K - $142K/yr

Senior Machine Learning Scientist The Senior Machine Learning Scientist is responsible for building ... Collaborative and customer-obsessed, with the ability to balance scientific rigor and engineering ...

Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to ...

Machine Learning Engineer

Bellevue, WA · On-site

$161.14 - $200/hr

Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to ...

Study and transform data science prototypes * Design machine learning systems * Research and ... implement appropriate ML algorithms and tools * Develop machine learning applications according to ...

Machine Learning Engineer

Bellevue, WA · On-site

$161.14 - $200/hr

Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to ...

Study and transform data science prototypes * Design machine learning systems * Research and ... implement appropriate ML algorithms and tools * Develop machine learning applications according to ...

Machine Learning Engineer

Seattle, WA · On-site

$175 - $308.50/hr

PhD in computer vision, computer graphics, machine learning, computer science, computer engineering or related fields. At Apple, base pay is one part of our total compensation package and is ...

Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to ...

Our Machine Learning and Data Science team are growing! We are looking to hire researchers and data scientists interested in breaking new ground to tackle some of the most complex customer experience ...

Responsibilities : • Study and transform data science prototypes • Design machine learning systems • Research and implement appropriate ML algorithms and tools • Develop machine learning ...

Machine Learning Engineer

Bellevue, WA · On-site

$161.14 - $200/hr

Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to ...

This involves developing sophisticated machine learning and large language models (LLMs) to ... You will also work with researchers and data scientists to develop, fine-tune, and evaluate domain ...

Machine Learning Manager

Seattle, WA · On-site

$180K - $250K/yr

Undergraduate or graduate degree in computer science or similar technical field * 4+ years experience as a machine learning engineer, with experience in training large deep learning models and ...

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Scientific Machine Learning information

See Seattle, WA salary details

$15

$35

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How much do scientific machine learning jobs pay per hour?

As of Aug 28, 2026, the average hourly pay for scientific machine learning in Seattle, WA is $35.82, according to ZipRecruiter salary data. Most workers in this role earn between $21.88 and $45.67 per hour, depending on experience, location, and employer.

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 Seattle, WA?

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

What cities near Seattle, WA are hiring for Scientific Machine Learning jobs?

Cities near Seattle, WA with the most Scientific Machine Learning job openings:

Machine Learning Engineer

Seattle, WA • On-site

$120K - $180K/yr

Full-time

Re-posted 28 days ago


Job description

The Role

We are looking for a Machine Learning Engineer to bridge the gap between AI research and production-grade flight systems. You will optimize, deploy, and scale machine learning models that directly impact Constellation’s orbital systems and ground operations.

Responsibilities

  • Deploy, monitor, and maintain ML models in production environments.

  • Build robust MLOps pipelines for continuous training and integration of models using telemetry data.

  • Optimize algorithms for low-latency inference on edge devices (spacecraft hardware).

  • Collaborate with data scientists and flight software engineers to integrate AI capabilities into core flight systems.

Requirements

  • B.S. or M.S. in Computer Science, Engineering, or equivalent experience.

  • Proven experience deploying machine learning models into production.

  • Strong software engineering skills in Python and C++.

  • Experience with cloud platforms, containerization (Docker), and MLOps tools.

Compensation Range: $120K - $180K