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

WGU

Senior Machine Learning Engineer

Raleigh, NC

$157K - $243K/yr

Master's degree in Computer Science, Software Engineering, Data Science, Machine Learning, Mathematics, Physics, or a related field; or equivalent relevant experience. * 5+ years of software ...

OptiTrack

Machine Learning & Operations Engineer

Durham, NC · Remote

$71K - $96K/yr

About the Role OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale an MLOps system and provide other support to teams working on projects involving machine learning.

OptiTrack

Machine Learning & Operations Engineer

Durham, NC · Remote

$67K - $90K/yr

About the Role OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale an MLOps system and provide other support to teams working on projects involving machine learning.

OptiTrack

Machine Learning & Operations Engineer

Durham, NC · Remote

$67K - $90K/yr

About the Role OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale an MLOps system and provide other support to teams working on projects involving machine learning.

Apple

AIML - Staff Machine Learning Engineer

Cary, NC · On-site

Building this system requires deep expertise in a myriad of cutting edge fields: search, natural language understanding, data engineering, machine learning, privacy preserving system design, and more.

RELX

Principal Machine Learning Engineer I

Raleigh, NC

$136K - $252K/yr

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.

RELX

Principal Machine Learning Engineer I

Raleigh, NC

$136K - $252K/yr

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.

Apple

AIML - Staff Machine Learning Engineer

Cary, NC

$212K - $386K/yr

Building this system requires deep expertise in a myriad of cutting edge fields: search, natural language understanding, data engineering, machine learning, privacy preserving system design, and more.

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Cary, NC

Machine Learning Engineer information

See Cary, NC salary details

$29.2K

$119.3K

$179.3K

How much do machine learning engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for machine learning engineer in Cary, NC is $119,293.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,000.00 and $143,600.00 per year, depending on experience, location, and employer.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What jobs make $3,000 a month without a degree?

A Machine Learning Engineer typically requires a degree, but roles such as data annotator, technical support specialist, or freelance programmer can sometimes earn around $3,000 monthly without a formal degree, especially with relevant skills and experience. These jobs often involve self-taught skills, online certifications, or on-the-job training and may require proficiency in tools like Python or cloud platforms.
More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Cary, NC? The most popular types of Machine Learning Engineer jobs in Cary, NC are:
What are popular job titles related to Machine Learning Engineer jobs in Cary, NC? For Machine Learning Engineer jobs in Cary, NC, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Cary, NC look for? The top searched job categories for Machine Learning Engineer jobs in Cary, NC are:
What cities near Cary, NC are hiring for Machine Learning Engineer jobs? Cities near Cary, NC with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Cary, NC as of June 2026, with employment types broken down into 100% Full Time. Highlights an 50% Hybrid, and 50% Remote job distribution, with an average salary of $119,293 per year, or $57.4 per hour.

Principal Machine Learning Engineer

Webex Events (formerly Socio)

Durham, NC • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Splunk AI Models Team

Splunk, a Cisco company, is building a safer, more resilient digital world with an end-to-end, full-stack platform designed for hybrid, multi-cloud environments. Join the AI Models team at Splunk, where we advance the state of AI for high-volume, real-time, multi-modal machine-generated data — including logs, time series, traces, and events. We combine deep AI research expertise with the scale and operational excellence of Splunk and Cisco's global engineering capabilities. Our work spans networking, security, observability, and customer experience — designing and deploying foundation models that enhance reliability, strengthen security, prevent downtime, and deliver predictive insights across Splunk Observability, Security, and Platform at enterprise scale. You'll be part of a culture that values technical excellence, impact-driven innovation, and cross-functional collaboration — all within a flexible, growth-oriented environment.

Your Impact
  • Set and Drive Vision: Define and champion the strategic vision for AI and foundation models across Splunk and Cisco platforms, shaping the research and technology roadmap to anticipate and address industry-defining challenges.
  • Architect and Lead Breakthroughs: Lead the end-to-end lifecycle of research, design, and deployment for large-scale foundation models targeting machine-generated data, with deep focus on logs and complementary modalities (time series, traces, events).
  • Influence at Scale: Partner with executive leadership, engineering, product, and data science teams to ensure AI solutions align with broader organizational objectives, product strategies, and customer needs.
  • Mentorship and Thought Leadership: Cultivate organizational excellence by mentoring senior technical talent, fostering research communities, and driving best practices in AI across global teams.
  • Foster Innovation: Embed cutting-edge research and technological advances into products, driving sustained competitive advantage and transformation at enterprise scale.
Minimum Qualifications:
  • PhD in Computer Science, or related quantitative field, plus 7+ years of industry research experience.
  • Proven track record in at least one of the following areas: large language modeling for both structure and unstructured data, deep learning-based time series modeling, advanced anomaly detection, and multi-modality modeling.
  • Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
  • Experience translating research ideas into production systems.
Preferred Qualifications:
  • Deep NLP & Domain-Adapted LLMs: Background in building and adapting large-scale language models (e.g., T5, BERT, LLaMA, GPTs) for specialized domains including structured/unstructured logs, text, and event sequences.
  • Log Analytics Expertise – In-depth knowledge of structured/unstructured system logs, event sequence analysis, anomaly detection, and root cause identification.
  • Advanced Anomaly Detection – Experience creating robust, scalable approaches (statistical, deep learning, or hybrid) for high-volume, real-time logs data.
  • Multi-Modal AI Modeling – Strong track record fusing logs, time series, traces, tabular data, and graphs for foundation models tackling complex operational insights.
  • Large-Scale Training & Optimization – Experience optimizing model architectures, distributed training pipelines, and inference efficiency to minimize cost and latency while preserving accuracy.
  • MLOps & Continuous Learning – Fluency in automated retraining, drift detection, incremental updates, and production monitoring of ML models.
  • Strong Research Track Record – Publications in top AI/ML conferences or journals (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, ACL, KDD) demonstrating contributions to state-of-the-art methods and real-world applications.

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