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Scientific Machine Learning Jobs (NOW HIRING)

Stefanini Group

Machine Learning Engineer

Dearborn, MI · On-site

Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy ...

Avride

Machine Learning Engineer

Austin, TX

Nice to have * Advanced degree in Computer Science, Machine Learning, Robotics, or a related field. * Experience developing ML algorithms for autonomous vehicles or robotics applications.

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Merican

Data Science & Machine Learning Engineer

$117K - $140K/yr

Senior Data Science & Machine Learning Engineer Location: Remote, USA (Client Location ZIP: 01730) Duration: 6 Months Contract to Hire We are seeking an experienced Senior Data Science & Machine ...

Stefanini Group

Machine Learning Engineer

Dearborn, MI · On-site

Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy ...

Calendly

Machine Learning Engineer

You will report to the head of Data Science & Machine Learning and will be responsible for building and operating ML-powered features that create magical experiences for our customers. Our team:

Avride

Machine Learning Engineer

Austin, TX · On-site

Preferred : • Advanced degree in Computer Science, Machine Learning, Robotics, or a related field. • Experience developing ML algorithms for autonomous vehicles or robotics applications. • ...

Apple

Machine Learning FEA Engineer

Bodega Bay, CA

$142K - $263K/yr

D. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline Publications in top journals or conferences Minimum Qualifications Strong Expertise in Machine Learning, Deep ...

Avride

Machine Learning Engineer

Austin, TX · On-site

Nice to have * Advanced degree in Computer Science, Machine Learning, Robotics, or a related field. * Experience developing ML algorithms for autonomous vehicles or robotics applications.

Apple

Machine Learning FEA Engineer

Bodega Bay, CA

$142K - $263K/yr

D. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline Publications in top journals or conferences Minimum Qualifications Strong Expertise in Machine Learning, Deep ...

Confie

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

... Scientists, Data Engineers, and Data Architects on production systems and applications Stay up-to-date with industry trends and advancements in artificial intelligence/machine learning On call ...

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Scientific Machine Learning information

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How much do scientific machine learning jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for scientific machine learning in the United States is $31.48, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $40.14 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 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 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.

More about Scientific Machine Learning jobs
What cities are hiring for Scientific Machine Learning jobs? Cities with the most Scientific Machine Learning job openings:
What states have the most Scientific Machine Learning jobs? States with the most job openings for Scientific Machine Learning jobs include:
What job categories do people searching Scientific Machine Learning jobs look for? The top searched job categories for Scientific Machine Learning jobs are:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $65,473 per year, or $31.5 per hour.
Machine Learning Engineer

Machine Learning Engineer

Stefanini Group

Dearborn, MI • On-site

Contractor

Posted 5 days ago

Job description

Stefanini Group is hiring!

Stefanini is looking for a Machine Learning Engineer(Dearborn, MI)

For quick apply, please reach out to Navneet Pathak at 248-213-3677/ navneet.pathak@stefanini.com

We are looking for candidates who will be responsible for predicting and/ or extracting meaningful trends/ patterns/ recommendations from raw data, leveraging data science methodologies including Machine Learning (ML), predictive modeling, math, statistics, advanced analytics, etc. 

Responsibilities

  • Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making 
  • Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making 
  • Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends 
  • Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation 
  • Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness

Skills Required:

  • Data Mining, Data/Analytics dashboards, ALGORITHMS, Data/Analytics, Data Analysis, Data Science

 

Experience Required:

  • 5+ experience in relevant field
  • Undertake statistical modeling, predictive analytics, and insight generation for the RAV platform 
  • Perform deep dive analyses on RAV vehicles to detect early indicators and recommend actions to prevent/mitigate RAV 
  • Execute quick turn studies for urgent business questions by mining, managing, and exploring large, high dimensional structured and unstructured datasets (e.g., RAV, quality, repair orders, repeat visits, parts orders/supply, service, warranty claims, recalls) 
  • Visualize validation results, analyses, and predictions using Power BI to deliver clear qualitative and quantitative insights 
  • Collaborate closely with architects, project managers, data engineers, software engineers, and other data scientists to test pipelines, validate outputs, and launch new platform capabilities and insights into production for maximum business value 
  • Communicate, present, and interpret analytical results to non technical audiences, cross functional teams, and leadership on a regular basis

                                             

Education Required

  • Master's Degree

Education Preferred

  • Doctorate

**Listed salary ranges may vary based on experience, qualifications, and local market. Also, some positions may include bonuses or other incentives***

Stefanini takes pride in hiring top talent and developing relationships with our future employees. Our talent acquisition teams will never make an offer of employment without having a phone conversation with you. Those face-to-face conversations will involve a description of the job for which you have applied. We will also speak with you about the process, including interviews and job offers.

About Stefanini Group

The Stefanini Group is a global provider of offshore, onshore and near shore outsourcing, IT digital consulting, systems integration, application, and strategic staffing services to Fortune 1000 enterprises around the world. Our presence is in countries like the Americas, Europe, Africa, and Asia, and more than four hundred clients across a broad spectrum of markets, including financial services, manufacturing, telecommunications, chemical services, technology, public sector, and utilities. Stefanini is a CMM level 5, IT consulting company with a global presence. We are a CMM Level 5 company.

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Education:Master (MA, MS...)Employment Type: CONTRACTOR

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