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Machine Learning Engineer Quantization Jobs in San Marcos, TX

webAI

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Job Summary : webAI is seeking a Senior Machine Learning Engineer to support their Public Sector ... quantization, pruning, distillation, and hardware specific acceleration. • Build and maintain ...

webAI

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Job Summary : webAI is seeking a Senior Machine Learning Engineer to support their Public Sector ... quantization, pruning, distillation, and hardware specific acceleration. • Build and maintain ...

webAI

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

They are seeking a Senior Machine Learning Engineer to transform prototype models into scalable ... quantization, pruning, distillation, and hardware specific acceleration. • Build and maintain ...

webAI Inc

Staff Machine Learning Engineer

Austin, TX · On-site +1

You will lead core product initiatives across on-device inference optimization, quantization, RAG ... Strong programming skills in Python and machine learning frameworks like TensorFlow and/or PyTorch.

Avride

Machine Learning Engineer

Austin, TX · On-site

Avride develops autonomous vehicle and delivery robot technology, and they are seeking an experienced Machine Learning Engineer to enhance their autonomous systems. The role involves developing and ...

Avride

Machine Learning Engineer

Austin, TX

About the role We are looking for an experienced Machine Learning Engineer with a strong background in developing and deploying modern machine learning solutions for complex real-world challenges. In ...

webAI Inc

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused ... Implement model optimization techniques such as quantization, pruning, distillation, and hardware ...

Avride

Machine Learning Engineer

Austin, TX · On-site

About the role We are looking for an experienced Machine Learning Engineer with a strong background in developing and deploying modern machine learning solutions for complex real-world challenges. In ...

Q2

Machine Learning Engineer

Austin, TX

SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and scaling AI solutions, while collaborating with cross-functional teams to advance machine learning ...

Q2 Software, Inc.

Machine Learning Engineer

Austin, TX · On-site

SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and scaling AI solutions, while collaborating with cross-functional teams to advance machine learning ...

LHi Group Ltd

Senior Machine Learning Engineer

Austin, TX · On-site

$335K - $400K/yr

* Senior Machine Learning Engineers needed for high growth tech company * Austin, TX - must be willing to work in office 4 days a week * High competitive salary + equity + strong benefits Senior ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in San Marcos, TX

Machine Learning Engineer Quantization information

See San Marcos, TX salary details

$28.5K

$116.6K

$175.2K

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

As of Aug 4, 2026, the average yearly pay for machine learning engineer quantization in San Marcos, TX is $116,585.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,900.00 and $140,300.00 per year, depending on experience, location, and employer.

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 San Marcos, TX? For Machine Learning Engineer Quantization jobs in San Marcos, TX, the most frequently searched job titles are:
What cities near San Marcos, TX are hiring for Machine Learning Engineer Quantization jobs? Cities near San Marcos, TX with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you

Senior Machine Learning Engineer

webAI

Austin, TX • On-site

$103K - $142K/yr

Full-time

Re-posted 16 days ago

Job description

Job Summary:
webAI is seeking a Senior Machine Learning Engineer to support their Public Sector initiatives focused on building and optimizing production-ready AI systems. The role involves transforming prototype models into scalable and reliable production systems that operate across various hardware environments.
Responsibilities:
• Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making across production systems.
• Productionize AI models from research prototypes into scalable, deployable systems used in real world applications.
• Engineer adaptive ML systems using LoRA, PEFT, and on-device inference strategies, leveraging PyTorch, TensorFlow, and Hugging Face Transformers for model development, fine-tuning, and optimization.
• Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration.
• Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval.
• Work with multi-modal AI systems across computer vision, audio, and natural language domains.
• Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions.
Qualifications:
Required:
• Active US Security clearance
• 4+ years of experience in applied AI, ML engineering, or production AI systems.
• Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers.
• Proven experience deploying AI models across cloud, edge, and mobile hardware environments.
• Expertise in model compression and optimization (quantization, pruning, distillation).
• Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone).
• Familiarity with multi-modal models and synthetic data generation methods.
• Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments.
Preferred:
• Experience with edge AI, federated learning, or offline inference systems.
• Understanding of AI governance and compliance frameworks relevant to public sector deployments.
• Experience integrating models into large scale distributed systems or microservice architectures.
• Excellent communication and technical documentation skills for collaboration across multi disciplinary teams.
• Strong understanding of GPU computing, CUDA, and performance profiling.
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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