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Ml Inference Jobs in Raleigh, NC (NOW HIRING)

Cisco

Applied AI Scientist

Durham, NC

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 ...

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

Cary, NC

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 ...

Berriehill Research

Senior AI Systems Engineer

Raleigh, NC · On-site +1

$92K - $126K/yr

Experience with AI/ML frameworks and tooling such as PyTorch, Hugging Face, or similar ecosystems ... Experience with model serving, inference optimization, or AI platform tools such as MLflow ...

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$36.5K

$119.3K

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How much do ml inference jobs pay per year?

As of Jul 16, 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 a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

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 engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

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.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

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.
What are popular job titles related to Ml Inference jobs in Raleigh, NC? For Ml Inference jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Raleigh, NC look for? The top searched job categories for Ml Inference jobs in Raleigh, NC are:
What cities near Raleigh, NC are hiring for Ml Inference jobs? Cities near Raleigh, NC with the most Ml Inference job openings:
Ml Inference jobs near you
Staff Software Engineer -- SubSystem Integration Test (SSIT)

Staff Software Engineer -- SubSystem Integration Test (SSIT)

Qualcomm

Raleigh, NC • On-site

$101K - $136K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Qualcomm rating

9.6

Company rating: 9.6 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

5th of 209 rated software companies

Job description

Job Summary:
Qualcomm Technologies, Inc. is seeking a Staff Software Engineer for SubSystem Integration Test (SSIT) as part of their AISW engineering team. In this role, you will own subsystem-level validation for the Delegates portfolio, define test strategies, and ensure feature quality before transitioning to QA and System Integration Test.
Responsibilities:
• Partner with delegate engineers from feature inception to define testability requirements, acceptance criteria, and subsystem test plans covering ORT QNN-EP, ExecuTorch HTP backend, and LiteRT delegate
• Own SSIT test strategy per feature: scope, entry/exit criteria, coverage targets, and risk-based prioritization — aligned with the development team before implementation begins
• Embed in sprint planning, design reviews, and code reviews to ensure features are architected for testability from day one
• Develop and maintain automated test content targeting the subsystem boundary: op and feature coverage, numerical accuracy, model-level functional correctness, and backend interface contracts
• Validate on-device behavior on Snapdragon SoCs using HIL infrastructure, covering functional correctness, latency, and memory under real hardware conditions
• Build Python/PyTest-based test suites integrated into the Delegates CI pipeline; ensure SSIT gates are enforced on every code change
• Develop and leverage AI-assisted tooling and agentic workflows (Claude Code, GitHub Copilot) to accelerate test content generation, coverage analysis, and failure triage
• Triage failures and isolate root causes across the delegate stack — ML framework → QNN runtime → HTP hardware — and drive resolution with development owners
• Flag integration risks early during feature development; distinguish subsystem-level issues from upstream framework bugs and downstream backend or system integration issues
• Define and own handoff criteria from SSIT to QA and SIT: documented test results, known issues, coverage gaps, and risk summaries that give downstream teams a clear picture of feature readiness
• Serve as the primary technical liaison between the Delegates development team and QA/SIT — translating feature context into actionable test guidance for downstream teams
• Maintain traceability between SSIT artifacts and QA/SIT test plans via JIRA; feed defects found in QA/SIT that trace to subsystem gaps back into SSIT coverage
Qualifications:
Required:
• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• Bachelor's + 8 years or Master's + 6 years in Software Engineering, Computer Science, Systems Engineering, or related field
• Strong Python; proven hands-on experience building automated test frameworks (PyTest or equivalent)
• C/C++ reading proficiency sufficient to debug delegate and runtime issues at the source level
• Demonstrated experience in subsystem or integration testing within a hardware/software product development cycle
• Experience with CI/CD platforms (Jenkins, GitHub Actions, or equivalent) and integrating test automation into build pipelines
• Strong analytical and debugging skills with ability to isolate failures across multi-layer software stacks
Preferred:
• Experience validating ML inference frameworks — ONNX Runtime, ExecuTorch, TFLite / LiteRT, or equivalent
• Hands-on experience with Qualcomm QNN, HTP/DSP, or Snapdragon SoC-based on-device validation
• Familiarity with model accuracy validation: quantization correctness, op-level numerical comparison, and tolerance analysis
• C/C++ proficiency for development and debug of ORT unit tests
• Cross-platform test experience: Linux, Android, and Windows (ARM64 or x86)
• Experience with JIRA defect tracking and Agile/scrum development practices
Company:
Qualcomm designs wireless technologies and semiconductors that power connectivity, communication, and smart devices. Founded in 1985, the company is headquartered in San Diego, USA, with a team of 10001+ employees. The company is currently Late Stage.

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About Qualcomm

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Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including 5G-enabled smartphones that double as pro-level cameras and gaming devices, smarter vehicles and cities, and the technology behind the smart, connected factories that manufactured your latest purchase. Our powerful connectivity solutions keep you connected—even in remote areas. Qualcomm 5G and AI innovations are the power behind the connected intelligent edge. You’ll find our technologies behind and inside the innovations that deliver significant value across multiple industries and to billions of people every day.

Industry

Technology, communication and media

Company size

10,000+ Employees

Headquarters location

San Diego, CA, US

Year founded

1985

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