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Scientific Machine Learning Jobs in Michigan (NOW HIRING)

Corning

$95K - $130K/yr

You will help translate data science prototypes into secure, scalable, and production-ready ... end machine learning pipelines covering data ingestion, preprocessing, training, validation ...

Varsity Tutors

Machine Learning Tutor

Kalamazoo, MI · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Ann Arbor, MI · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Detroit, MI · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Saanvi Technologies

Machine Learning Engineer 3

Dearborn, MI · On-site

$105K - $126K/yr

Machine Learning Engineering Engineer 3 Dearborn, MI W2 Position Description: We are seeking an ... This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable ...

Domino's Pizza

Lead Machine Learning Engineer

Ann Arbor, MI · On-site

$100K - $132K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Provide technical leadership and mentorship to engineers and data scientists. * Translate business ... Develop and operationalize machine learning and Generative AI solutions that support business ...

New

Crowe LLP

Machine Learning Intern

Grand Rapids, MI

$27 - $42/hr

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will ...

Crowe LLP

Machine Learning Intern

Grand Rapids, MI

$27 - $42/hr

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will ...

Doist

Machine Learning Engineer, II - 3D Perception

Ann Arbor, MI · On-site

$153 - $184/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

What You'll Need to Succeed Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 3+ years of relevant industry experience, OR ...

New

OpTech

Scientist-Research Engineer (ML)

Dearborn, MI · On-site

The ideal candidate will leverage data science, machine learning, physics-based modeling, and signal processing techniques to predict component degradation and estimate Remaining Useful Life (RUL ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

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Showing results 21-40

All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in Michigan

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 (09235)

Talent Management Plus, Inc.

Detroit, MI • On-site

$110 - $170/hr

Other

Posted 6 days ago

Job description

Position: Machine Learning Engineer (09235)Location: Detroit, MIDuration: 6 months+Summary:

The Data Science and Analytics Center of Excellence (COE) is responsible for leading the creation and development of the overall strategy and direction of data science and advanced analytics – including ensuring continuity and seamless extension of existing programs, the development of a short- and long-term vision and roadmap, and defining and institutionalizing the role that data and analytics play throughout the organization as the fuel that drives and shapes client’s priorities and serves as an accelerant for client’s progress.

Description:

The ML Engineer is a key player in the Integrated Tech Programs & Strategies team. This role will be responsible for data engineering, data science Model deployment, testing and management for the end-to-end ML and data pipeline including data products. This role will leverage Client’s AI labs environment to enable the delivery in a common data lake and products.

Responsibilities:
  • Responsible for building and managing end-to-end data pipelines and operations from ingestion and integration through delivery for the data science prototypes and data products.
  • Adept at queries, report writing and presenting findings, analyze large complex datasets to extract insights and decide on the appropriate technique.
  • Understand and use data and ML fundamentals, including data structures, algorithms, computability and complexity and computer architecture.
  • Collaborate with data engineers to build data and model pipelines, manage the infrastructure and data pipelines needed to bring code to production.
  • Provide support to engineers and product managers in implementing machine learning in the product.
  • Drive the design, building and launching of new data models and ML/Data pipelines in production.
  • Identify, analyze, and interpret trends or patterns in complex data sets.
  • Consulting with managers, Product owners to determine and refine machine learning objectives.
  • Transforming data science prototypes and applying appropriate ML tools and technologies.
  • Contribute and support the development of the overall data science and machine learning strategy and roadmap.
Required Skills:
  • Bachelor’s degree in computer science, or equivalent IT knowledge/experience.
  • 2+ years of relevant work experience in Data Analysis, Data Engineer, Data Science & Data Integration.
  • Must have strong data infrastructure, data engineering and Machine Learning skills
  • Must have a proven track record of leading and scaling data pipelines, ML Model deployments in a cloud/on prem/big data environment
  • Strong knowledge of and experience with reporting packages (Business Objects etc.), databases (SQL etc.), programming (XML, JavaScript, or ETL frameworks).
  • Programming languages: SQL, Spark, Python, R, Jupyter Notebooks, Java, Scala, C++
  • Data Exploration and ETL: Alteryx, Talend, H2O, Informatica, Data Stage, Azure Data explorer, Azure Data Factory.
  • Data Warehouse Solutions: Redshift, Snowflake, Postgres, Data Lake.
  • Big Data technologies: Azure, AWS, Hadoop, Spark, Hive, Kafka, Flume, NoSQL stores (HBase, Cassandra, DynamoDB, MongoDB).
  • Cloud storage: S3, GCS, ADLS, Blob.
  • Machine Learning: Cloudera Data Science Workbench, Azure ML, Amazon ML, Google AutoML, Vertex AI.
  • Data Visualization Solutions: MS Power BI, Looker, Tableau, Azure Streaming Analytics, Data Lake Analytics, Azure Time Series Insights, Azure Synapse Analytics.
  • CI/CD and Code Management: Git, Maven, Docker, Jenkins, Azure Dev Ops.
  • Experience working with Data engineering, Data science, ETL teams and managing implementing projects that utilize big data, advanced analytics, and machine learning technologies.
  • Hands-on experience in building data and ML pipelines from variety of sources such as data warehouses and in-memory OLAP models, as well as experience in NoSQL/cloud.
  • Strong understanding of data, ML Models, Big Data, Relational databases, streaming and batch data processing.
  • Knowledge of machine learning evaluation metrics and best practice.
  • Strong experience building end-to-end data view with focus on integration.
Preferred Skills:
  • Experience working with on-prem and cloud-based data warehouses
  • Experience with cloud-based personalization and machine-learning applications.
  • Experience working for consumer or business-facing digital brands.
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