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

Cigna

Data Science Director - Remote

Raleigh, NC · On-site +1

As the Data Science Director for Pricing & Underwriting, you will lead high-impact teams that build ... Provide technical leadership across machine learning, statistical modeling, feature engineering ...

New

AstraZeneca

Agentic AI Scientist

Durham, NC · On-site

PhD in computer science, data science, artificial intelligence, machine learning or related fields. * At least 3 years of experience in Deep Learning and ML * Excellent coding skills in languages ...

AstraZeneca

Agentic AI Scientist

Durham, NC · On-site

PhD in computer science, data science, artificial intelligence, machine learning or related fields. * At least 3 years of experience in Deep Learning and ML * Excellent coding skills in languages ...

LexisNexis

Manager Data Science

Raleigh, NC · On-site +1

$115K - $192K/yr

Lead the development of advanced AI and machine learning models to solve complex business problems. * Collaborate with other data scientists and engineers to design, develop, and deploy AI solutions.

RELX Group plc

Manager Data Science

Raleigh, NC · On-site

$115K - $192K/yr

Lead the development of advanced AI and machine learning models to solve complex business problems. * Collaborate with other data scientists and engineers to design, develop, and deploy AI solutions.

LexisNexis

Manager Data Science

Raleigh, NC · On-site

$115K - $192K/yr

Lead the development of advanced AI and machine learning models to solve complex business problems. * Collaborate with other data scientists and engineers to design, develop, and deploy AI solutions.

RELX

Manager Data Science

Raleigh, NC · On-site +1

$115K - $192K/yr

Lead the development of advanced AI and machine learning models to solve complex business problems. * Collaborate with other data scientists and engineers to design, develop, and deploy AI solutions.

Tata Consultancy Services

AI ML Data Scientist

Cary, NC · On-site

Required : • BACHELOR OF COMPUTER SCIENCE • 8 - 10 Years of experience • Machine Learning techniques • Unsupervised - K-means Clustering, PCA - Dimension Reduction, Kernel Density Estimations ...

LexisNexis

Manager Data Science

Raleigh, NC

$115K - $192K/yr

Lead the development of advanced AI and machine learning models to solve complex business problems. * Collaborate with other data scientists and engineers to design, develop, and deploy AI solutions.

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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 9, 2026, the average hourly pay for scientific machine learning in Raleigh, NC is $30.60, according to ZipRecruiter salary data. Most workers in this role earn between $18.70 and $39.04 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.

What are popular job titles related to Scientific Machine Learning jobs in Raleigh, NC? For Scientific Machine Learning jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Raleigh, NC look for? The top searched job categories for Scientific Machine Learning jobs in Raleigh, NC are:
What cities near Raleigh, NC are hiring for Scientific Machine Learning jobs? Cities near Raleigh, NC with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Raleigh, NC as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $63,645 per year, or $30.6 per hour.
Senior Machine Learning Engineer III ***Raleigh, NC***

Senior Machine Learning Engineer III ***Raleigh, NC***

LexisNexis

Raleigh, NC • On-site

$118K - $219K/yr

Full-time

Re-posted 19 days ago

LexisNexis rating

7.6

Company rating: 7.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

155th of 442 rated business services

Job description

Are you looking to develop your Machine Learning Engineer career?
Do you enjoy coaching others to achieve high standards?
This is a full-time position based in Raleigh, NC.
(Hybrid - 3 days in office)
About the Role
We are seeking a Consultant-level Machine Learning Engineer to lead the implementation and scaling of AI systems for legal products. This role focuses on how to build and scale-owning system architecture, infrastructure, and productionization of ML/LLM solutions.
You will partner with Data Scientists to turn validated models and prototypes into reliable, high-performance, customer-facing systems.
Key Responsibilities
  • Architect and implement scalable ML/LLM systems in production.
  • Build and deploy LLM applications, including RAG pipelines and agentic systems.
  • Implement hybrid search systems (semantic + lexical) using embeddings and search platforms.
  • Develop and maintain APIs, microservices, and model serving infrastructure.
  • Build data pipelines and streaming systems for large-scale data processing.
  • Define and develop reusable frameworks, libraries, and infrastructure for AI/ML across teams.
  • Optimize systems for latency, scalability, reliability, and cost efficiency.
  • Establish best practices for deployment, monitoring, observability, and CI/CD.
  • Collaborate with Data Scientists to productionize models and integrate into products.
  • Provide technical leadership in system design and engineering standards.

Required Qualifications
  • Bachelor's degree in Computer Science, Engineering, or a related field.
  • Strong experience implementing and scaling production ML/LLM systems.
  • Deep experience with LLM application development, including RAG and prompt orchestration.
  • Strong experience designing and implementing agentic systems using agent frameworks (e.g., LangChain, LangGraph, AutoGen, Google ADK), including orchestration of multi-step workflows in production environments.
  • Strong experience with hybrid search (semantic + lexical), embeddings, and search platforms (e.g., Solr, OpenSearch).
  • Expertise in distributed systems and cloud-native development, including AWS (S3, DynamoDB).
  • Experience with streaming and messaging systems (e.g., Kafka, SQS) and caching (e.g., Redis).
  • Proficiency in Python and experience with systems languages (e.g., Rust, Go, Scala).
  • Experience building scalable APIs (REST/GraphQL).
  • Experience with containerization and orchestration (Docker, Kubernetes).
  • Strong software engineering fundamentals (system design, testing, CI/CD).

Preferred Qualifications
  • Experience with LLM platforms (e.g., ChatGPT/OpenAI, Claude, Gemini, LangChain, Google ADK).
  • Experience with DevOps and infrastructure as code (e.g., Terraform, CloudFormation, Jenkins).
  • Experience with big data technologies (e.g., Spark, Hadoop).
  • Familiarity with graph databases (e.g., Dgraph, Neo4j, Neptune).
  • Experience building high-availability, low-latency systems.
  • Experience in legal or regulatory domains.

Key Competencies
  • Strong system architecture and scalability mindset.
  • Ownership of implementation, performance, and reliability.
  • Ability to translate data science solutions into production systems.
  • Cross-functional collaboration with DS, product, and platform teams.
  • Excellent debugging, optimization, and operational skills.
  • Clear communication of technical designs and trade-offs.

#AIFluent
U.S. National Base Pay Range: $118,300 - $219,800. Geographic differentials may apply in some locations to better reflect local market rates.This job is eligible for an annual incentive bonus.
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