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Openvino Jobs (NOW HIRING)

ICONSTAFF

Machine Learning Engineer

Cambridge, MA · On-site +1

Experience with ONNX, TensorRT, or OpenVINO for deployment. * Robotics middleware (ROS2). * SLAM, 3D perception, or sensor fusion (LiDAR, IMU). * Real-time or low-latency inference pipelines. Why ...

Ford Motor Company

Software Engineer

Redford, MI

Experience with OpenCV, TensorRT, or OpenVINO for vision optimization. * Familiarity with ML frameworks like PyTorch or TensorFlow . * Knowledge of industrial protocols (MQTT, WebSockets) for real ...

EnCharge AI

$175K - $245K/yr

PyTorch, TensorFlow, ONNX Runtime, TVM, TensorRT, or OpenVINO. * Understanding of low-level hardware acceleration (e.g., SIMD, AVX, Tensor Cores, VNNI). * Familiarity with compiler optimizations for ...

Viant Technology

Senior IoT Engineer

Boston, MA

$140K - $160K/yr

Experience with AI inference optimization technologies such as TensorRT, OpenVINO, ONNX Runtime, TensorFlow Lite, or similar frameworks. LIFE AT VIANT * Investing in our employee's professional ...

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Openvino information

What is the difference between Openvino vs Computer Vision Engineer?

AspectOpenvinoComputer Vision Engineer
Required CredentialsKnowledge of AI frameworks, hardware optimization, programming skillsDegree in Computer Science, Electrical Engineering, or related fields; experience in AI and image processing
Work EnvironmentTech companies, AI development labs, hardware manufacturersResearch institutions, tech firms, startups, or industry-specific companies
Employer & Industry UsagePrimarily used in AI inference optimization, embedded systems, and hardware accelerationDeveloping and implementing computer vision algorithms for applications like surveillance, robotics, and autonomous vehicles

Openvino focuses on optimizing AI inference workloads and hardware acceleration, often requiring knowledge of AI frameworks and hardware. In contrast, a Computer Vision Engineer designs and develops vision algorithms, working across various industries. While both roles involve AI and image processing, Openvino is more specialized in deployment and optimization, whereas Computer Vision Engineers focus on algorithm development and application.

What is OpenVINO and what is it used for?

OpenVINO (Open Visual Inference and Neural Network Optimization) is a free toolkit developed by Intel to help developers optimize and deploy AI inference, particularly deep learning models, across Intel hardware such as CPUs, GPUs, VPUs, and FPGAs. It is mainly used for accelerating computer vision applications like image classification, object detection, and facial recognition. OpenVINO streamlines the process of converting and optimizing models from popular frameworks, making them faster and more efficient for edge and cloud deployments.

What job makes $10,000 a month without a degree?

High-paying jobs that can reach $10,000 a month without a degree often include roles such as software developers, sales managers, or skilled trades like electricians and plumbers, especially with experience and certifications. Success in these roles typically depends on skills, performance, and industry demand rather than formal education alone.

What jobs pay 2000 a day?

High-paying jobs that can pay around $2,000 a day typically include specialized roles such as senior software engineers, data scientists, management consultants, and certain freelance or contract positions in fields like technology, finance, or healthcare. These roles often require advanced skills, certifications, or extensive experience, and may involve project-based or consulting work with flexible schedules.

What are some common challenges faced by professionals working with OpenVINO in deploying AI models to edge devices?

Professionals working with OpenVINO often encounter challenges related to optimizing and converting AI models to ensure they run efficiently on diverse edge hardware. These challenges include ensuring compatibility with various device architectures, minimizing inference latency, and handling limitations in memory and compute resources. Additionally, troubleshooting model conversion errors and maintaining accuracy during optimization are frequent tasks. Collaboration with hardware engineers and software developers is also essential to address performance bottlenecks and integrate solutions smoothly into production environments.

Who owns OpenVINO?

OpenVINO is a toolkit developed and maintained by Intel Corporation, designed to optimize deep learning models for deployment on various hardware platforms. As a product of Intel, it is owned and supported by the company, which provides updates, documentation, and support for users and developers working with the toolkit.

Which 3 jobs will survive AI?

Openvino professionals working in AI and machine learning are likely to find roles in data science, AI model development, and AI infrastructure maintenance will persist due to their reliance on complex problem-solving and specialized skills. These jobs require continuous learning, programming knowledge, and understanding of AI frameworks, making them more resilient to automation. Skills in deep learning, neural networks, and cloud computing further support their longevity in the evolving tech landscape.

What are the key skills and qualifications needed to thrive as an OpenVINO Developer, and why are they important?

To thrive as an OpenVINO Developer, you need a solid background in computer vision, deep learning, and proficiency in Python or C++, often supported by a degree in computer science or a related field. Familiarity with the OpenVINO toolkit, neural network optimization, and frameworks like TensorFlow or PyTorch is essential. Strong problem-solving skills, attention to detail, and effective communication help developers collaborate and innovate in deploying AI solutions. These skills ensure optimized AI performance on edge devices and successful integration of machine learning models into production environments.
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Openvino jobs near you
Infographic showing various Openvino job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 74% Physical, and 26% Remote job distribution.

Senior Machine Learning Engineer - Fine-Tuning and On-device AI

HP IQ

Palo Alto, CA • On-site

$120K - $215K/yr

Other

Posted 8 days ago

Job description

About the Role 

We are seeking a Senior Machine Learning Engineer to lead the fine-tuning, optimization, and deployment of AI models for diverse tasks, with a strong emphasis on on-device inference. You will work on cutting-edge applications such as orchestration, planning, multi-agent coordination, and other intelligent decision-making systems. 

You will be responsible for adapting foundation models (LLMs, multimodal models) to specialized domains, making them fast, accurate, and efficient for resource-constrained environments-while ensuring robustness and safety. 

What You Might Do

  • Model Fine-Tuning & Adaptation 
  • Fine-tune large language models, multimodal models, and task-specific models for orchestration, planning, and any other workflows as defined. 
  • Design and run experiments to improve task accuracy, robustness, and generalization. 
  • Explore and apply methods like full fine-tuning, LoRA, QLoRA and other types of parameter-efficient fine-tuning. 
  • Employee advanced techniques such as QAT, DPO, GRPO to further improve the model quality. 
  • On-Device Optimization 
  • Prune, quantize and compress models (e.g., INT8, INT4, mixed-precision) for CPU, GPU, NPU and edge accelerators. 
  • Optimize models for low-latency inference using frameworks like OpenVINO, ONNX Runtime, QNN etc..
  • Data Pipeline & Deployment 
  • Build robust data pipelines for domain-specific datasets, including synthetic data generation and annotation. 
  • Define evaluation metrics. Perform evaluations and analyze results. 
  • Establish best practices for versioning, reproducibility, and continuous improvement of model performance. 
  • AI Orchestration & Planning 
  • Develop and refine models to support multi-step reasoning, tool orchestration, and decision planning. 
  • Work with stakeholders on orchestrator architecture. 
  • Collaborate with product and research teams to design intelligent, context-aware assistant capabilities. 

Essential Qualifications

  • 7+ years of experience in applied machine learning, including at least 3 years in LLM fine-tuning. 
  • Proficiency in Python and ML frameworks ecosystem (HuggingFace, PyTorch). 
  • Strong understanding of transformer architectures, attention mechanisms, and PEFT techniques. 
  • Experience with on-device inference optimization (OpenVINO, ONNX, QNN). 
  • Familiarity with orchestration/planning architectures and techniques for AI assistants. 
  • Track record of delivering production-ready ML solutions in latency-sensitive environments. 

Preferred Qualifications

  • Experience with multi-agent systems or AI assistant orchestration. 
  • Familiarity with advanced inference optimization techniques such as KV cache paging , flash attention. 
  • Knowledge about common inference engines, including but not limited to llama.cpp, vLLM. 

Salary Range:  $120,000 - $215,000

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