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Machine Learning Engineer Quantization Jobs in Louisiana

Pwc

NGA AI Engineer Manager

New Orleans, LA

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Pwc

NGA AI Engineer Senior Manager

New Orleans, LA

$91K - $321K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Pwc

US Tech - AI Engineering Manager

New Orleans, LA

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

CGI Inc.

Senior AWS Cloud Engineer

Lafayette, LA · On-site

$53.25 - $71.25/hr

Senior AWS Cloud Engineer Category: Infrastructure/Cloud Main location: United States, Louisiana ... AWS Machine Learning * AWS Machine Learning * Cloud architecture * Docker * GitLab What you can ...

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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 Louisiana? For Machine Learning Engineer Quantization jobs in Louisiana, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Louisiana look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Louisiana are:
What cities in Louisiana are hiring for Machine Learning Engineer Quantization jobs? Cities in Louisiana with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
Senior ASIC post-silicon validation engineer (m/f/d)

Senior ASIC post-silicon validation engineer (m/f/d)

Advantest

Hall Summit, LA

Full-time

Posted 16 days ago

Job description

Join a global, highly skilled engineering team at the heart of Advantest's cutting edge IC test solutions. As a Senior ASIC Post-Silicon Validation Engineer, you will help validate and characterize key technologies that enable the next generation of semiconductor testing.
Therefore, you develop and align test plans and develop test solutions for the ASICs designed for the Advantest V93000 Semiconductor Test System. The parts to be validated range from analog, RF, power and complex high-speed digital.
In this role, you will collaborate closely with chip design engineers, software engineers, product marketing, and other engineering disciplines. You will translate requirements into robust designs, solve challenging technical problems, and deliver high quality results on schedule and at scale.
Job Duties & Responsibilities

  • Develop test solutions for devices with applications in large high-speed digital, analog RF or power segments.
  • Test and characterize functionality and performance of both internally and externally developed ASICs of all types.
  • Responsible for the scoping, design, and technical validity of test solutions.
  • Work within RD and with other functions to extract requirements.
  • Support production implementation and execution of the developed test solutions.
  • Write test programs for data analysis using Machine Learning
  • BS in Electrical Engineering/Physics/Computer Science or equivalent with over 5 years semiconductor test experience.
  • Strong device- and test-program coding and debugging skills in V93k SMT8 and smart RDI
  • Knowledge of digital, mixed signal, RF and power device test methodologies
  • Ability to quickly pick up Java test program development with high level APIs
  • Knowledge of Linux and demonstrated experience with Java programming
  • Python scripting and knowledge in Data Analysis and Machine Learning
  • Ability to work in a fast paced, project oriented, team environment.
  • Positive attitude and excellent communication skills are a must.

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