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Machine Learning Engineer Quantization Jobs in Kansas

Knowmadics

Senior Machine Learning Engineer

Wichita, KS · On-site

$93K - $128K/yr

Wichita, KS; Lawton OK; or Round Rock, TX Job Purpose/Summary The Machine Learning Engineer will build and integrate machine learning solutions into our next-generation space and critical ...

Knowmadics

Senior Machine Learning Engineer

Wichita, KS

$93K - $128K/yr

As the first dedicated internal Machine Learning Engineer for this product, they willplay acriticalrole inrequirements generation, team leadership, andinfluencing the future of our products. This is ...

Bespoke Labs

Machine Learning Engineer

Pittsburg, KS · 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

Wichita, KS · 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

Topeka, KS · 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 ...

PICTOR LABS INC

$89K - $123K/yr

Prototype and productionize new inference optimization techniques, including quantization, pruning ... Strong software engineering practices including version control (Git), CI/CD, unit testing, and ...

Overstory

Role & Team As a Staff Machine Learning Engineer at Overstory, you will lead the development and scaling of our Wildfire Fuel Detection Model. This core engine powers how we understand vegetation ...

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

Machine Learning Engineer

Keysight Technologies, Inc.

Topeka, KS • On-site

Full-time

Re-posted 2 days ago

Keysight Technologies rating

8.1

Company rating: 8.1 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

40th of 141 rated electronics manufacturers

Job description

Overview
Keysight is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.
Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.
We are seeking a Machine Learning Engineer to lead the design, development, and deployment of scalable machine learning models that power business decisions across the enterprise. This role combines technical depth in ML/AI with a strong understanding of business domains such as Sales, Service, Finance, Order Fulfillment, and Supply Chain. You will collaborate closely with Data Scientists, Data Engineers, and business partners to build production-ready solutions that drive measurable impact.
Responsibilities
1. Machine Learning Development & Deployment
  • Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross-sell/upsell opportunity identification.
  • Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.
  • Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

2. Cross-Functional ML Use Cases
  • Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.
  • Develop domain-specific models and continuously improve them using feedback loops and real-world performance data.

3. Model Governance and MLOps
  • Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.
  • Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).

4. Data Engineering and Feature Architecture
  • Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.
  • Perform feature selection, transformation, and engineering aligned with each domain's business logic.

5. Communication & Stakeholder Collaboration
  • Present technical insights and model results to business and executive stakeholders in a clear, actionable format.
  • Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.

Qualifications
Required:
  • 4-6 years of experience in machine learning, data science, or AI engineering, with a strong software engineering foundation.
  • Proficiency in Python, and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar.
  • Experience deploying models into production using ML pipelines and orchestration frameworks.
  • Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).

Preferred:
  • Experience supporting business functions such as Finance, Sales, or Operations with ML use cases.
  • Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store).
  • Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).
  • Background in statistics, forecasting, optimization, or recommendation systems.

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Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***

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