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Machine Learning Engineer Quantization Jobs in Ohio

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Collaborate closely with the MLOps, product teams, business stakeholders, machine learning ... Model quantization for LLMs (GPTQ, AWQ, bitsandbytes); GPU memory optimization techniques (tensor ...

Collaborate closely with the MLOps, product teams, business stakeholders, machine learning ... Model quantization for LLMs (GPTQ, AWQ, bitsandbytes); GPU memory optimization techniques (tensor ...

Machine Learning Engineer Machine Learning Engineering Delivery Location: Blue Ash, OH Competencies: 10+ years experience required Agile Way of Working Digital: Machine Learning Digital: Artificial ...

As an AI Engineer, you will be responsible for designing, implementing, testing and maintaining ... The ideal candidate will have a strong background in AI, machine learning and data science, with ...

Senior AI/ML Engineer

Columbus, OH · Remote

$90 - $100/hr

Remote Our client seeks a Senior AI/ML Engineer to design and deliver cloud-native machine learning solutions on AWS. The role includes LLM orchestration, RAG pipelines, vector database integration ...

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Machine Learning Engineer Quantization information

What are some common challenges Machine Learning Engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

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

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

What does a Machine Learning Engineer Quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

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

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What are popular job titles related to Machine Learning Engineer Quantization jobs in Ohio? For Machine Learning Engineer Quantization jobs in Ohio, the most frequently searched job titles are:
What cities in Ohio are hiring for Machine Learning Engineer Quantization jobs? Cities in Ohio with the most Machine Learning Engineer Quantization job openings:

AI Staff Machine Learning Engineer -Gen AI,Machine Learning,Graph ML,Big Data(10030)

Extreme Networks

New Hampshire, OH

Full-time

Posted 22 days ago


Job description

Over 50,000 customers globally trust our end-to-end, cloud-driven networking solutions. They rely on our top-rated services and support to accelerate their digital transformation efforts and deliver unprecedented progress. With double-digit growth year over year, no provider is better positioned to deliver scalable outcomes than Extreme.

Inclusion is one of our core values and in our DNA. We are committed to fostering an inclusive workplace that embraces our differences and creates an atmosphere where all our employees thrive because of their differences, not in spite of them.

Become part of Something big with Extreme! As a global networking leader, learn why there’s no better time to join the Extreme team.

Position : AI Staff Machine Learning Engineer -Gen AI,Machine Learning,Graph ML,Big Data
Experience : 5 to 14 Years
Hybrid/Remote
 
Our AI Core group is pioneering platforms and solutions for Generative AI, including AI Agents, RAG, Knowledge Bases, Data Mining, Anomaly Detection, and LLM fine-tuning. These innovations power flagship Extreme products while enabling entirely new offerings. Together, we are driving a fundamental shift in how businesses manage networks by building intelligent, high-performance multi-agent systems that perceive, learn, and act in real time. At Extreme, innovation is not just encouraged, it is expected. Advance with us and help shape the future of network intelligence. 
About the position
  • Be a thought leader and forward thinker, help drive an innovative vision for our various products and platforms, design and launch strategic machine learning (ML) solutions and drive business-wide innovation.
  • Take the lead in the end-to-end software development lifecycle, encompassing design, testing, deployment, and operations, lead technical discussions and strategy, and participate hands-on in design reviews, code reviews, and implementation.
  • Craft high-performance, high-scale microservices architectures, including synchronous and asynchronous web services.
  • Develop real-time online inferencing for highly complex models using Triton, TensorRT and mixed precision computing.
  • Mentor and develop other engineers on the team, establish technical direction and foster team culture.
  • Uphold the highest standards of technical rigor in engineering and operational excellence, build highly resilient and scalable systems, and champion operational and process improvements.
Basic Qualifications:
  • Degree in mathematics/computer science or related discipline.
  • 5 to 10 years of experience in the complete software development lifecycle including design, coding, code reviews, testing, build processes, deployments and operations.
  • 5 to 10 years of experience in Python with an in-depth knowledge of its advanced features and libraries.
  • Expertise in designing RESTful APIs with hands-on experience with technologies such as FastAPI.
  • Proficient in Docker, Kubernetes, and modern CI/CD practices.
  • 3+ years of experience in leading the design and architecture of large distributed systems preferably on cloud platforms (e.g., AWS, Azure, Google Cloud).
  • Experience as a mentor, tech lead or leading an engineering team.
Preferred Qualifications:
  • MS or PhD in Computer Science or equivalent experience in ML.
  • Experience working with ML technologies (PyTorch, Sagemaker, Triton, TensorRT, etc.).
  • Experience with NoSQL and document databases.
  • Proven ability to handle big data, optimize workflows, and improve system performance.
  • Come work with a team of highly talented engineers, and advance with us to achieve new heights every day!
 
  • Salary based on qualifications, experience and region up to USD 170 k to 240 K plus benefits.
Extreme Networks, Inc. (EXTR) creates effortless networking experiences that enable all of us to advance. We push the boundaries of technology leveraging the powers of machine learning, artificial intelligence, analytics, and automation. Over 50,000 customers globally trust our end-to-end, cloud-driven networking solutions and rely on our top-rated services and support to accelerate their digital transformation efforts and deliver progress like never before. For more information, visit Extreme's website or follow us on Twitter, LinkedIn, and Facebook.

We encourage people from underrepresented groups to apply. Come Advance with us! In keeping with our values, no employee or applicant will face discrimination/harassment based on: race, color, ancestry, national origin, religion, age, gender, marital domestic partner status, sexual orientation, gender identity, disability status, or veteran status. Above and beyond discrimination/harassment based on “protected categories,” Extreme Networks also strives to prevent other, subtler forms of inappropriate behavior (e.g., stereotyping) from ever gaining a foothold in our organization. Whether blatant or hidden, barriers to success have no place at Extreme Networks.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.