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Ml Inference Jobs in Raleigh, NC (NOW HIRING)

Northern Base

AI Solution Lead

Cary, NC · On-site

... AI/ML models for real-time and batch inference ✔ Hands-on experience with Kubernetes, Helm, containerization, and cloud-native deployments ✔ Experience building event-driven, resilient ...

Motion Recruitment

Lead Machine Learning Engineer

Raleigh, NC · On-site

$91K - $120K/yr

Define reference architecture for LLM, ML, and agent-based systems across products * Design high-availability, low-latency inference platforms for global scale * le.Establish reusable platform ...

New

Deloitte

AI Engineer Consultant

Raleigh, NC · Hybrid

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...

NetApp

Principal Product Manager

Morrisville, NC

Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) * RAG ... ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment ...

Cisco

Applied AI Scientist

Durham, NC · On-site

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

Cisco

Applied AI Scientist

Cary, NC · On-site

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

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All JobsMl Inference JobsMl Inference Jobs in Raleigh, NC

Ml Inference information

See Raleigh, NC salary details

$36.5K

$119.3K

$191K

How much do ml inference jobs pay per year?

As of Aug 6, 2026, the average yearly pay for ml inference in Raleigh, NC is $119,312.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,800.00 and $132,200.00 per year, depending on experience, location, and employer.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.
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Ml Inference jobs near you
Infographic showing various Ml Inference job openings in Raleigh, NC as of August 2026, with employment types broken down into 92% Full Time, 3% Part Time, and 5% Contract. Highlights an 82% Physical, 4% Hybrid, and 14% Remote job distribution, with an average salary of $119,312 per year, or $57.4 per hour.

AI Solution Lead

Northern Base

Cary, NC • On-site

Full-time

Posted 6 days ago

Job description

Role: AI Solution Lead

Location: Cary, NC (Onsite)

Employment Type: Full-Time

Visa Type: USC / GC Only

✅ Must-Have Qualifications:

✔ 8–10+ years of experience in AI/ML, Software Engineering, or Cloud-Native Application Development (13+ years overall IT experience preferred)

✔ Strong experience building and productionizing cloud-native backend services and AI/LLM inference pipelines

✔ Expertise in Python, FastAPI, asynchronous programming, and microservices architecture

✔ Hands-on experience building Agentic AI applications using LangChain and LangGraph

✔ Strong experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, vector databases, prompt engineering, and context engineering

✔ Experience packaging, deploying, serving, and monitoring AI/ML models for real-time and batch inference

✔ Hands-on experience with Kubernetes, Helm, containerization, and cloud-native deployments

✔ Experience building event-driven, resilient distributed systems and API integrations

✔ Strong knowledge of CI/CD pipelines, automated testing, observability, monitoring, and Service Level Objectives (SLOs)

✔ Strong understanding of systems design including concurrency, caching, reliability, scalability, and rate limiting

✔ Experience with Azure Cloud, Azure Kubernetes Service (AKS), and managed cloud services is preferred

✔ Knowledge of model governance, model monitoring, performance tuning, and data engineering best practices

✔ Experience collaborating with cross-functional engineering, AI, and frontend teams

✔ Bachelor's Degree in Computer Science or a related field