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Machine Learning Engineer Quantization Jobs in Ravenel, SC

BSFR Property Management LLC

Data Scientist

Charleston, SC · On-site

$90 - $140/hr

The ideal candidate combines strong expertise in data science, machine learning, and statistical ... Programming: strong Python and SQL skills for building models and analyzing data, with hands‑on ...

DXC Technology

Data Engineering Lead

Charleston, SC · On-site

Build and optimize data environments that support AI, machine learning, and advanced analytics ... Partner with engineering, product management, operations, and business teams to prioritize ...

DXC Technology

Data Engineering Lead

Charleston, SC · On-site

Build and optimize data environments that support AI, machine learning, and advanced analytics ... Partner with engineering, product management, operations, and business teams to prioritize ...

Maymont Homes

Data Scientist

Charleston, SC · On-site

$110 - $160/hr

The Data Scientist leverages advanced analytics, statistical modeling, machine learning, and AI to ... Programming: Strong Python and SQL skills for building models and analyzing data, with hands-on ...

Maymont Homes

Data Scientist

Charleston, SC · On-site

The Data Scientist leverages advanced analytics, statistical modeling, machine learning, and AI to ... Programming: Strong Python and SQL skills for building models and analyzing data, with hands-on ...

Brookfield Properties

Data Scientist

Charleston, SC · Remote

The Data Scientist leverages advanced analytics, statistical modeling, machine learning, and AI to ... Programming: Strong Python and SQL skills for building models and analyzing data, with hands-on ...

Maymont Homes

Data Scientist

Charleston, SC · Remote

The Data Scientist leverages advanced analytics, statistical modeling, machine learning, and AI to ... Programming: Strong Python and SQL skills for building models and analyzing data, with hands-on ...

Brookfield Properties

Data Scientist

Charleston, SC · On-site

The Data Scientist leverages advanced analytics, statistical modeling, machine learning, and AI to ... Programming: Strong Python and SQL skills for building models and analyzing data, with hands-on ...

HII

ENGINEER MANUFACTURING 4

Goose Creek, SC · On-site

$66K - $85K/yr

HII's diverse workforce includes skilled tradespeople; artificial intelligence, machine learning (AI/ML) experts; engineers; technologists; scientists; logistics experts; and business administration ...

HII

ENGINEER MANUFACTURING 4

Goose Creek, SC

$66K - $85K/yr

HII's diverse workforce includes skilled tradespeople; artificial intelligence, machine learning (AI/ML) experts; engineers; technologists; scientists; logistics experts; and business administration ...

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Showing results 21-40

All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Ravenel, SC

Machine Learning Engineer Quantization information

See Ravenel, SC salary details

$31.8K

$130.1K

$195.6K

How much do machine learning engineer quantization jobs pay per year?

As of Aug 20, 2026, the average yearly pay for machine learning engineer quantization in Ravenel, SC is $130,140.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,600.00 and $156,600.00 per year, depending on experience, location, and employer.

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 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 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 Ravenel, SC?

For Machine Learning Engineer Quantization jobs in Ravenel, SC, the most frequently searched job titles are:

Machine Learning Engineer Quantization jobs near you
Infographic showing various Machine Learning Engineer Quantization job openings in Ravenel, SC as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 25% Part Time, 1% Contract, and 1% Nights. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution, with an average salary of $130,140 per year, or $62.6 per hour.

Generative AI Automation Engineer - Remote Job

EnthuZiastic

Charleston, SC • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

About Us

Our mission is to bring people together and connect them into a community to nurture each other. We aim to share a conducive environment, a joyous space to grow and excel; a world brimming with selfless love and enough kindness. We strive to enrich each of our lives with kaleidoscopic memories we make here - vibrant, lively, of all hues and colors.

Job Description

​

This is a remote position.

We are seeking a highly skilled and innovative Generative AI Automation Engineer to join our team. The ideal candidate will be responsible for designing, developing, and implementing automation solutions powered by Generative AI models. This role requires a combination of expertise in machine learning, natural language processing, software engineering, and automation frameworks to drive efficiency and innovation in business processes.

Key Responsibilities:

Generative AI Model Implementation:

  • Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks.

  • Integrate pre-trained models or build custom models for specific use cases.

Automation Design and Development:

  • Design and implement AI-driven workflows and solutions to automate repetitive tasks and improve process efficiency.

  • Develop APIs, scripts, and tools for seamless integration of AI models into existing systems.

Data Management:

  • Collect, preprocess, and analyze large datasets for training and validating AI models.

  • Ensure data privacy and compliance with regulatory requirements during data handling.

System Integration:

  • Collaborate with software development and IT teams to integrate Generative AI solutions with enterprise systems.

  • Build and maintain pipelines for real-time AI inference and automation.

Monitoring and Optimization:

  • Continuously monitor AI automation solutions to ensure accuracy, efficiency, and reliability.

  • Optimize models and processes based on performance metrics and user feedback.

Research and Innovation:

  • Stay updated with the latest advancements in Generative AI and automation technologies.

  • Identify opportunities for implementing cutting-edge AI solutions to address business challenges.

Documentation and Collaboration:

  • Document technical designs, workflows, and implementation strategies.

  • Collaborate with cross-functional teams, including product managers, data scientists, and software engineers.

Requirements

Required Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

  • Strong programming skills in Python, with experience in frameworks like TensorFlow, PyTorch, or Hugging Face.

  • Proficiency in designing and deploying machine learning models, particularly in Generative AI.

  • Experience with automation tools (e.g., RPA, workflow orchestration tools).

  • Familiarity with cloud platforms (AWS, Azure, or Google Cloud) and containerization technologies (Docker, Kubernetes).

  • Solid understanding of data structures, algorithms, and software design principles.

  • Strong analytical and problem-solving skills.

  • Excellent communication and teamwork abilities.

Preferred Qualifications:

  • Experience with NLP, image generation, or multimodal AI models.

  • Hands-on experience with APIs for AI services like OpenAI, Cohere, or Google AI.

  • Familiarity with prompt engineering and fine-tuning Generative AI models.

  • Knowledge of MLOps practices for deploying and maintaining AI solutions.

  • Previous experience in automation or workflow optimization projects.

Benefits

Why Join Us?

  • Work with cutting-edge Generative AI technologies.

  • Collaborate with a team of forward-thinking innovators.

  • Make a tangible impact on the future of automation and AI-driven processes.

If you are passionate about leveraging Generative AI to create innovative automation solutions, we invite you to apply and be a part of our dynamic and growing team.

Apply