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Scientific Machine Learning Jobs in Cary, NC (NOW HIRING)

Peter Millar LLC

Senior Data Scientist

Durham, NC · On-site

$90 - $120/hr

The Senior Data Scientist expands Peter Millar's data science capacity beyond customer analytics ... Operating at the intersection of applied machine learning, business strategy, and the modern data ...

Epic Games

Machine Learning Engineer, Memory

Cary, NC · On-site

PhD in Computer Science, Mathematics or related field, or 3+ years of relevant industry experience * Experience creating machine learning algorithms for vision and/or language problems and deploying ...

Epic Games

Machine Learning Engineer, Memory

Cary, NC · On-site

PhD in Computer Science, Mathematics or related field, or 3+ years of relevant industry experience * Experience creating machine learning algorithms for vision and/or language problems and deploying ...

LexisNexis

Senior Data Scientist III

Raleigh, NC · On-site

They are seeking a Senior Data Scientist to lead AI and machine learning model development, analyze large datasets, and mentor junior team members. Responsibilities : • Working closely with other ...

LexisNexis

Senior Data Scientist III

Raleigh, NC · On-site

They are seeking a Senior Data Scientist to lead AI and machine learning model development, analyze large datasets, and mentor junior team members. Responsibilities : • Working closely with other ...

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All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in Cary, NC

Scientific Machine Learning information

See Cary, NC salary details

$12

$29

$48

How much do scientific machine learning jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for scientific machine learning in Cary, NC is $29.16, according to ZipRecruiter salary data. Most workers in this role earn between $17.84 and $37.21 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 Cary, NC?

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

What job categories do people searching Scientific Machine Learning jobs in Cary, NC look for?

The top searched job categories for Scientific Machine Learning jobs in Cary, NC are:

What cities near Cary, NC are hiring for Scientific Machine Learning jobs?

Cities near Cary, NC with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Cary, NC as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 22% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $60,655 per year, or $29.2 per hour.

Principal Machine Learning Engineer I

LexisNexis

Raleigh, NC • On-site

$136K - $252K/yr

Full-time

Re-posted 9 days ago

LexisNexis rating

7.6

Company rating: 7.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

189th of 492 rated business services

Job description

About our Team
LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (www.relx.com), a global provider of information-based analytics and decision tools for professional and business customers. Our company has been a long-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi-model approach that prioritizes using the best model from today's top model creators for each individual legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (https://stories.relx.com/responsible-ai-principles/index.html).
About the Role
Do you love collaborating with teams to solve complex technical problems?
We are seeking a Principal Machine Learning Engineer to design, build, and operate scalable AI/ML systems and agentic architectures that support next-generation legal research and analytics products. This role combines deep ML expertise with distributed systems engineering and AI platform development.
You will play a key role in developing enterprise-grade AI systems, including large language model (LLM) infrastructure, retrieval-augmented generation (RAG) pipelines, and autonomous agent frameworks designed for complex large unstructured data.
Responsibilities:
  • Provide architectural direction and code-level guidance.
  • Establish engineering best practices for ML system design, testing, and deployment.
  • Conduct design reviews, performance reviews, and technical roadmap planning.
  • Architect distributed ML systems serving multiple global products.
  • Standardize infrastructure patterns for LLM serving and retrieval systems.
  • Define and implement enterprise-ready agentic frameworks.
  • Architect multi-step reasoning systems.
  • Lead decisions on deterministic workflows vs. autonomous agents.
  • Implement guardrails, safety layers, and traceability mechanisms.
  • Develop evaluation frameworks to measure reasoning quality, hallucination rates, and reliability.
  • Establish CI/CD standards for ML lifecycle management.
  • Ensure compliance with enterprise data governance and responsible AI standards.

Requirements
  • 10 + years of Machine Learning/Software Engineer experience
  • Master's degree or bachelor's degree, computer science degree is highly desirable.
  • Strong software engineering background with experience in building system design, architecting AI feature/products that caters large number of users and deals with large volume of unstructured data
  • Experience with ML deployment to production
U.S. National Base Pay Range: $136,100 - $252,800. Geographic differentials may apply in some locations to better reflect local market rates.This job is eligible for an annual incentive bonus.
We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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