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Scientific Machine Learning Jobs in Michigan (NOW HIRING)

Howmet

Machine Learning Engineer

Whitehall, MI · On-site

MS or PhD degree from an accredited university in Engineering, Data Science, Computer Science, Machine Learning, Materials Science, Mathematics, Statistics, or Analytics. * Minimum of 2 years of ...

Stefanini

Machine Learning Engineer

Allen Park, MI · On-site

... science methodologies including Machine Learning (ML), predictive modeling, math, statistics, advanced analytics, etc. Key ResponsibilitiesUnderstand business requirements and analyze datasets to ...

Corning

You will partner with data scientists, analytics leaders, IT, and manufacturing teams to move ... machine learning pipelines, including data ingestion, preprocessing, training, validation ...

Stefanini

Machine Learning Engineer

Dearborn, MI · On-site

Stefanini is looking for a Machine Learning Engineer(Dearborn, MI) For quick apply, please reach ... Data Mining, Data/Analytics dashboards, ALGORITHMS, Data/Analytics, Data Analysis, Data Science ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI

Machine Learning Engineer Location: Detroit, MI- Onsite Type: Full-time Security Clearance: No ... Required Qualifications * BS. in Computer Science, or related field. * 3+ years of professional ...

FastTek

Machine Learning Engineer

Dearborn, MI · On-site

Machine Learning Engineer #1058742 Position Description: We are seeking an experienced AI Engineer ... This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable ...

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $180K/yr

Desired Qualifications * 2-8+ years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing-or a strong recent graduate with ...

Workiva

$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 Michigan?

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

What job categories do people searching Scientific Machine Learning jobs in Michigan look for?

The top searched job categories for Scientific Machine Learning jobs in Michigan are:

What cities in Michigan are hiring for Scientific Machine Learning jobs?

Cities in Michigan with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 22% Part Time, 2% Temporary, 2% Contract, and 1% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer

Whitehall, MI • On-site

Howmet Aerospace
Aviation • 10K+ employees
Learn more about Howmet Aerospaceexternal

Full-time

Posted 4 days ago

Howmet Aerospace rating

7.8

Company rating: 7.8 out of 10

Based on 166 frontline employees who took The Breakroom Quiz

49th of 72 rated aerospace companies

Job description

Howmet Aerospace is hiring a Machine Learning Engineer with expertise in deep learning to join our innovative Research and Development team. This role involves building cutting-edge machine learning and deep learning applications and collaborating with cross-functional teams to support our casting, alloy, core, and rings facilities.

The position is located in Whitehall, Michigan in our Howmet Research Center (HRC).

Primary Responsibilities

  • Design, develop, and evaluate advanced deep learning architectures (e.g., CNNs, Mask R-CNNs, YOLO) to address complex manufacturing challenges.
  • Train and optimize generative models to accelerate development (e.g., diffusion models, VAEs, GANs).
  • Build and refine machine learning algorithms to enhance Howmet products across all business units.
  • Construct, manipulate, and analyze large datasets using tools such as Python and SQL.
  • Leverage transfer learning techniques and pretrained models to achieve strong performance with limited data.
  • Fine‑tune existing models for specific applications and datasets.
  • Develop customized deep learning architectures to handle diverse data types.
  • Conduct statistical multi-factor analyses to uncover complex relationships and improve manufacturing processes.
  • Present data-derived conclusions to a non-technical audience.
  • Identify opportunities to optimize processes and implement continuous improvement tools using machine learning.
  • Promote a data-driven culture across the organization by expanding machine learning applications and leading training initiatives.
  • Collaborate with internal customers to validate trials, implement process enhancements, and integrate machine learning into production workflows.

Basic Qualifications:
•    MS or PhD degree from an accredited university in Engineering, Data Science, Computer Science, Machine Learning, Materials Science, Mathematics, Statistics, or Analytics.
•    Minimum of 2 years of hands-on experience in deep learning and machine learning demonstrating ability to apply advanced statistical methods and machine learning algorithms to production/field data using Python.
•    Experience with feature engineering techniques (feature creation, selection, and transformation).
•    Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position.
•    This export- control language can be added directly to the job posting in the Job Info box of the posting by selecting the sentence in the dropdown field titled "Export-Controlled Data" in section 4 of the job requisition

Preferred Qualifications:
•    Minimum of 5 years of professional experience in data science or machine learning.
•    Theoretical understanding and practical experience applying reinforcement learning techniques for real time decision making and control.
•    Experience using generative models such as diffusion models, VAEs and GANs
•    Comprehensive knowledge of advanced analytics and machine learning techniques.
•    Strong statistical background with demonstrated expertise in analyzing industrial/manufacturing data.
•    Familiarity with manufacturing or industrial plant environments and their unique challenges.
•    Knowledge of software development life cycle (SDLC) 
•    Proficiency in visualization tools (e.g., Power BI)
•    Exceptional verbal and written communication skills with the ability to convey complex ideas clearly.
•    Strong organizational skills and the ability to work independently and in a cross-functional team environment.

Why Join Us?
•    Be at the forefront of innovation, applying machine learning to aerospace manufacturing.
•    Collaborate with a supportive, cutting-edge team dedicated to solving challenging real-world problems.
•    Access professional growth opportunities and a chance to directly impact Howmet Aerospace’s success.

What Howmet Aerospace employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Howmet Aerospace

Sourced by ZipRecruiter

Howmet Aerospace Inc. (NYSE: HWM), headquartered in Pittsburgh, Pennsylvania, is a leading global provider of advanced engineered solutions for the aerospace and transportation industries. The Company's sales for 2021 approximated $5 billion. The Company's primary businesses focus on jet engine components, aerospace fastening systems, titanium structural parts and forged wheels. With nearly 1,150 granted and pending patents, the Company's differentiated technologies promote more fuel efficiency for aircraft and commercial transportation. Howmet is proud to be an Equal Employment Opportunity and Affirmative Action employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Industry

Aviation

Company size

10,000+ Employees

Headquarters location

Pittsburgh, PA, US

Year founded

1888

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