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Scientific Machine Learning Jobs (NOW HIRING)

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 ...

Aston Carter

Machine Learning Engineer

Manhattan, NY · On-site

$125/hr

Essential Skills * 5+ years of professional experience in Data Science, Machine Learning, Advanced Analytics, Quantitative Research, Data Engineering, or related fields. * Strong proficiency in ...

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 ...

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:

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. • ...

Socket.dev

Machine Learning Engineer

Los Angeles, CA · On-site

$180 - $250/hr

Role Description Founding Data Scientist / Machine Learning Engineer We're looking for a highly ambitious Data Scientist to help build the predictive intelligence layer behind nowfluence. This is not ...

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 ...

Team Velocity Marketing

$160 - $190/hr

Senior Data Scientist - Machine Learning & AI Senior Data Scientist - Machine Learning & AI Remote | Full-Time | $160,000-$190,000 Team Velocity is seeking a Senior Data Scientist to develop and ...

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 ...

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Scientific Machine Learning information

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$13

$31

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How much do scientific machine learning jobs pay per hour?

As of Sep 3, 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 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.

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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 August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $65,473 per year, or $31.5 per hour.

Machine Learning Engineer

Stefanini

Allen Park, MI • On-site

Other

Re-posted 18 days ago

Key responsibilities

  • Analyze datasets and understand business requirements to determine suitable approaches for data-driven decision-making.

  • Design and implement data analysis and machine learning models, hypotheses, algorithms, and experiments.

  • Apply analytics techniques such as data mining, predictive modeling, and advanced analytics to uncover patterns, relationships, and trends.

Job description


Stefanini Group is hiring!
Stefanini is looking for a Machine Learning Engineer(Allen Park, MI)
For quick apply, please reach out to Navneet Pathak at /
We are looking for a candidate who is 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.
Key ResponsibilitiesUnderstand 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:J2EE, Logistics, Python, Machine Learning
Skills Preferred:AIPGEE, Advance data Migration, API, Data Management
Experience Required:5+ years of experience in relevant fieldFully Functional Middleware API: A production-ready middleware layer that ingests, aggregates, and exposes the 6 core operational data points. Agentic AI Orchestrator: A deployed multi-agent system that autonomously monitors the middleware data, flags anomalies, and generates structured recommendation JSON payloads. Human-in-the-Loop (HITL) Dashboard Integration: APIs and webhooks that feed the AI's reasoning, recommendations, and confidence scores into our front-end application. Co-Developed Codebase: A clean, modular, and fully tested Git repository co-authored with our internal team. Coaching Playbook: A comprehensive training package and transfer-of-ownership document for our internal engineering and product teams
Experience PreferredAutomotive Supply Chain - Logistics
Education RequiredBachelor's Degree
Education PreferredCertification Program
**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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