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Machine Learning Engineer Quantization Jobs in Iowa

We work closely with engineering, product, design, data engineering, machine learning operations, and LLM engineering teams to translate complex AI research into production-ready features used by ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

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Machine Learning Engineer Quantization information

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 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 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 Iowa? For Machine Learning Engineer Quantization jobs in Iowa, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Iowa look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Iowa are:
Infographic showing various Machine Learning Engineer Quantization job openings in Iowa as of May 2026, with employment types broken down into 1% Internship, 55% Full Time, 41% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 79% Physical, 7% Hybrid, and 14% Remote job distribution.
US Tech - AI Engineering Senior Associate

US Tech - AI Engineering Senior Associate

Pwc

Des Moines, IA

$55K - $187K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 19 days ago


PwC rating

8.3

Company rating: 8.3 out of 10

Based on 73 frontline employees who took The Breakroom Quiz

20th of 57 rated business consultants


Job description

Industry/Sector

Not Applicable

Specialism

Data Science

Management Level

Senior Associate

Job Description & Summary

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.
Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven decision making. You will work on developing predictive models, conducting statistical analysis, and creating data visualisations to solve complex business problems.
Focused on relationships, you are building meaningful client connections, and learning how to manage and inspire others. Navigating increasingly complex situations, you are growing your personal brand, deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients, and to deliver quality. Embracing increased ambiguity, you are comfortable when the path forward isn't clear, you ask questions, and you use these moments as opportunities to grow.
Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:
Respond effectively to the diverse perspectives, needs, and feelings of others.
Use a broad range of tools, methodologies and techniques to generate new ideas and solve problems.
Use critical thinking to break down complex concepts.
Understand the broader objectives of your project or role and how your work fits into the overall strategy.
Develop a deeper understanding of the business context and how it is changing.
Use reflection to develop self awareness, enhance strengths and address development areas.
Interpret data to inform insights and recommendations.
Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.
The Opportunity
As part of the People Tech & AI practice you will design, build, and deploy AI
- and agent-based solutions that modernize workforce, HR, and people experience platforms. As a Senior Associate you will analyze complex problems, mentor others, and maintain elevated standards while supporting enterprise-scale transformation initiatives for clients and internal stakeholders. This role offers
The Opportunity
to work hands-on across the AI solution lifecycle, contributing to impactful projects that drive innovation.
Responsibilities
- Support enterprise-scale transformation initiatives for clients
- Analyze complex problems and provide strategic insights
- Mentor and guide junior associates in their roles
- Maintain rigorous standards of quality and compliance in deliverables
- Navigate complex situations to achieve project goals
What You Must Have
- Bachelor's Degree
- At least 3 years of experience in software, platform, or cloud engineering
- In lieu of a Bachelor's Degree, demonstrating, in addition to the minimum years of experience required for the role, three years of specialized training and/or progressively responsible work experience in technology for each missing year of college
What Sets You Apart
- Applying hands-on experience with Azure services and APIs
- Building scalable platforms with a focus on reliability
- Collaborating with designers, product owners, and data scientists to deliver People Tech & AI solutions
- Learning new technologies quickly and applying them
- Contributing to platform standards and documentation
- Monitoring platform health and troubleshooting issues
- Creating and executing automated tests for validation
- Designing, developing, and deploying conversational bots using Azure Bot Framework SDK and Composer, integrating Azure Cognitive Services (LUIS/Orchestrator)
- Implementing dialog flows with strong state management, proactive messaging, and secure authentication using Azure AD/SSO

Travel Requirements

Up to 20%

Job Posting End Date

The salary range for this position is: $55,000 - $151,470. For residents of Washington state the salary range for this position is: $55,000 - $187,000. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus. PwC offers a wide range of benefits, including medical, dental, vision, 401k, holiday pay, vacation, personal and family sick leave, and more. To view our benefits at a glance, please visit the following link: https://pwc.to/benefits-at-a-glanceAs PwC is anequal opportunity employer, all qualified applicants will receive consideration for employment at PwC without regard to race; color; religion; national origin; sex (including pregnancy, sexual orientation, and gender identity); age; disability; genetic information (including family medical history); veteran, marital, or citizenship status; or, any other status protected by law.PwC does not intend to hire experienced or entry level job seekers who will need, now or in the future, PwC sponsorship through the H-1B lottery, except as set forth within the following policy: https://pwc.to/H-1B-Lottery-Policy.Learn more about how we work: https://pwc.to/how-we-workFor only those qualified applicants that are impacted by the Los Angeles County Fair Chance Ordinance for Employers, the Los Angeles' Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, San Diego County Fair Chance Ordinance, and the California Fair Chance Act, where applicable, arrest or conviction records will be considered for Employment in accordance with these laws. At PwC, we recognize that conviction records may have a direct, adverse, and negative relationship to responsibilities such as accessing sensitive company or customer information, handling proprietary assets, or collaborating closely with team members. We evaluate these factors thoughtfully to establish a secure and trusted workplace for all.Applications will be accepted until the position is filled or the posting is removed, unless otherwise set forth on the following webpage. Please visit this link for information about anticipated application deadlines: https://pwc.to/us-application-deadlines

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