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Machine Learning Engineer Associate 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 ...

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 Associate information

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$37K

$73.7K

$117.7K

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

As of Jul 9, 2026, the average yearly pay for machine learning engineer associate in Kansas is $73,699.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,400.00 and $84,700.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Machine Learning Engineer Associates when deploying models to production?

Machine Learning Engineer Associates often encounter challenges such as ensuring model scalability, managing data pipeline reliability, and addressing issues with model drift after deployment. Collaborating closely with data engineers and software developers is essential to integrate models seamlessly into existing systems. Additionally, balancing model performance with resource constraints and maintaining clear documentation for reproducibility are important aspects of the role. Gaining familiarity with deployment tools and best practices can help overcome these hurdles.

What are Machine Learning Engineer Associates?

Machine Learning Engineer Associates are entry-level professionals who help design, build, and maintain machine learning models and systems. They typically work under the guidance of senior engineers, assisting in data preprocessing, model training, and testing. Their responsibilities may include implementing algorithms, evaluating model performance, and deploying solutions to production environments. This role requires a strong foundation in programming, statistics, and machine learning principles, often acquired through education or internships.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Associate, and why are they important?

To thrive as a Machine Learning Engineer Associate, you need a solid understanding of programming (especially Python), mathematics, and foundational machine learning concepts, typically supported by a relevant degree or coursework. Familiarity with tools and frameworks like TensorFlow, PyTorch, scikit-learn, and experience with version control systems such as Git are essential. Strong problem-solving abilities, communication skills, and a collaborative mindset help you work effectively within technical teams. These competencies ensure you can develop, implement, and improve machine learning models that deliver actionable insights and drive business value.
What are the most commonly searched types of Machine Learning Engineer jobs in Kansas? The most popular types of Machine Learning Engineer jobs in Kansas are:
What are popular job titles related to Machine Learning Engineer Associate jobs in Kansas? For Machine Learning Engineer Associate jobs in Kansas, the most frequently searched job titles are:
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Machine Learning Engineer Associate 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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