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Machine Learning Engineer Quantization Jobs in Louisiana

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

New

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

New

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

New

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

New

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

New

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Machine Learning Engineer Quantization information

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 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 Machine Learning Engineer

Bollinger Shipyards

Raceland, LA • On-site

$99K - $136K/yr

Full-time

Re-posted 17 days ago

Bollinger Shipyards rating

6.5

Company rating: 6.5 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

Job description

Job Title: Senior Machine Learning Engineer

Location: Mulitple Locations

Position Overview:

The Senior ML Engineer is responsible for operationalizing machine learning and AI solutions into scalable, reliable, and production-ready enterprise systems. This role bridges data science, software engineering, and infrastructure disciplines to deploy, monitor, optimize, and support AI solutions that drive operational and business outcomes.

Key Responsibilities: 

•             Deploy, integrate, and maintain machine learning and AI solutions within enterprise workflows and operational systems

•             Design and develop scalable ML pipelines, feature stores, APIs, and model-serving infrastructure

•             Collaborate with Data Scientists to productionize models and improve deployment readiness

•             Monitor model performance, drift, availability, and reliability across production environments

•             Implement processes for model retraining, versioning, governance, and lifecycle management

•             Partner with Data Engineering teams to support feature engineering and data pipeline integration

•             Ensure ML solutions are secure, scalable, maintainable, and aligned with enterprise architecture standards

•             Support AI applications across forecasting, operational optimization, bidding, scheduling, maintenance, and automation use cases

•             Troubleshoot and resolve issues related to model deployment and operational performance

•             Contribute to ML engineering standards, best practices, and platform improvements

•             Document architecture, deployment processes, and operational support procedures

 

Qualifications: 

·       Bachelor’s degree in Computer Science, Software Engineering, Data Science, or related field

·       6–10 years in ML or software engineering

·       Strong Python and ML deployment experience

·       Experience with cloud ML systems

 

Skills: 

•             Experience with Azure ML, Databricks, ML Ops, or similar cloud AI platforms

•             Experience in manufacturing, industrial, operational, or engineering environments

•             Familiarity with large language models, Generative AI, and intelligent automation

•             Experience supporting enterprise AI applications integrated with ERP or operational systems

•             Knowledge of monitoring, observability, and model governance practices

•             Experience with Docker, Kubernetes, and infrastructure-as-code practices

Bollinger is an equal opportunity employer and is committed to providing employment opportunities to minorities, females, veterans and disabled individuals, and without regard to sexual orientation and gender identity. 

What Bollinger Shipyards employees say

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