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Machine Learning Engineer Quantization Jobs in Texas

webAI

Staff Machine Learning Engineer

Austin, TX · On-site

They are seeking an experienced Staff Machine Learning Engineer with a strong background in Large Language Models and Mixture of Experts to lead the development and optimization of advanced AI models ...

CVS Health

Machine Learning Engineer

Irving, TX · On-site +1

$96K - $144K/yr

Aetna Resources, LLC., a CVS Health company, is hiring for the following role in Irving, TX: Machine Learning Engineer to build, deploy, and monitor artificial intelligence (AI)/machine learning (ML ...

Vitol

Senior Machine Learning Engineer

Houston, TX · On-site

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing ...

Vitol

Senior Machine Learning Engineer

Houston, TX

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing ...

Vitol

Senior Machine Learning Engineer

Houston, TX

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing ...

Chevron

Lead Machine Learning Engineer

Houston, TX

$97K - $128K/yr

Chevron is seeking a Machine Learning Engineer to build and scale production AI solutions that drive critical decisions across subsurface and wells operations. In this role, you will partner with ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring ...

Chevron

Lead Machine Learning Engineer

Houston, TX · On-site

$97K - $128K/yr

Chevron is seeking a Machine Learning Engineer to build and scale production AI solutions that drive critical decisions across subsurface and wells operations. In this role, you will partner with ...

K-Tek Resourcing LLC

Sr. Machine Learning Engineer

Austin, TX · On-site

$113K - $136K/yr

Don'''''t apply OPT holder, please Machine Learning Engineer Responsibilities * Design, develop, train, and deploy machine learning models to solve business problems. * Build and maintain scalable ...

Bespoke Labs

Machine Learning Engineer

Arlington, TX · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Austin, TX · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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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 Texas? For Machine Learning Engineer Quantization jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Texas look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Texas are:
What cities in Texas are hiring for Machine Learning Engineer Quantization jobs? Cities in Texas with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you

Staff Machine Learning Engineer

webAI

Austin, TX • On-site

Full-time

Posted 10 days ago

Job description

Job Summary:
webAI is a software company that is building a decentralized AI development platform. They are seeking an experienced Staff Machine Learning Engineer with a strong background in Large Language Models and Mixture of Experts to lead the development and optimization of advanced AI models while collaborating with cross-functional teams.
Responsibilities:
• Lead the development and optimization of Large Language Models and Mixture of Experts models.
• Collaborate with cross-functional teams to integrate ML models into our platform.
• Conduct cutting-edge research in machine learning, with a focus on improving the performance and efficiency of LLMs.
• Stay abreast of the latest advancements in AI and ML, and apply this knowledge to improve our models and methodologies.
• Mentor junior engineers and contribute to the team’s knowledge sharing and best practices.
Qualifications:
Required:
• Advanced degree (Ph.D. preferred) in Computer Science, or a related field.
• Proven track record of innovations through publications or industry experience.
• Minimum of 5 years of experience in machine learning, with specific expertise in Large Language Models and Mixture of Experts.
• Strong programming skills in Python and machine learning frameworks like TensorFlow and/or PyTorch.
• Demonstrated ability to lead complex projects and work collaboratively in a team environment.
• Excellent problem-solving skills and a passion for innovation.
Preferred:
• Experience with cloud computing services (AWS, Azure, GCP).
• Knowledge of Big Data technologies (Hadoop, Spark).
• Familiarity with containerization and orchestration technologies (Docker, Kubernetes).
• Publications or presentations in recognized Machine Learning journals or conferences.
Company:
The leader in private AI. Founded in 2020, the company is headquartered in Austin, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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