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Machine Learning Engineer Quantization Jobs in Orlando, FL

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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Strong Python programming skills with hands-on experience building, training, deploying, and monitoring machine learning models. * Solid experience with SQL (2+ years) for database querying, data ...

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Strong Python programming skills with hands-on experience building, training, deploying, and monitoring machine learning models. * Solid experience with SQL (2+ years) for database querying, data ...

Data Engineer

Orlando, FL · On-site

$101K - $136K/yr

Exposure to machine learning or analytics use cases. * Experience working with large-scale cloud ... Data Engineering Employment Type: Full time Primary City, State, Region, Postal Code: Orlando, FL ...

Data Scientist- Associate

Orlando, FL · On-site

$55K - $55K/yr

Required : • Minimum one year of experience or strong internship/academic projects in data science, machine learning, or software engineering • Minimum of a Bachelor's degree is required • ...

Data Scientist

Orlando, FL · On-site

$107K/yr

Proficiency with data mining, statistical analysis, and machine learning platforms (e.g., AzureML). • Programming: Expert-level skills in SQL, Python, and R. • Automation & Apps: Experience with ...

Showing results 21-40

Machine Learning Engineer Quantization information

See Orlando, FL salary details

$29.4K

$120.2K

$180.6K

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

As of Aug 22, 2026, the average yearly pay for machine learning engineer quantization in Orlando, FL is $120,208.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,800.00 and $144,700.00 per year, depending on experience, location, and employer.

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 Orlando, FL?

For Machine Learning Engineer Quantization jobs in Orlando, FL, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Quantization jobs in Orlando, FL look for?

The top searched job categories for Machine Learning Engineer Quantization jobs in Orlando, FL are:

What cities near Orlando, FL are hiring for Machine Learning Engineer Quantization jobs?

Cities near Orlando, FL with the most Machine Learning Engineer Quantization job openings:

US Tech - AI Engineering Manager

Pwc

Orlando, FL

$73K - $244K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 2 days ago


PwC rating

8.3

Company rating: 8.3 out of 10

Based on 76 frontline employees who took The Breakroom Quiz

26th of 72 rated business consultants


Job description

Industry/Sector

Not Applicable

Specialism

Data Science

Management Level

Manager

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.
Enhancing your leadership style, you motivate, develop and inspire others to deliver quality. You are responsible for coaching, leveraging team member's unique strengths, and managing performance to deliver on client expectations. With your growing knowledge of how business works, you play an important role in identifying opportunities that contribute to the success of our Firm. You are expected to lead with integrity and authenticity, articulating our purpose and values in a meaningful way. You embrace technology and innovation to enhance your delivery and encourage others to do the same.
Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:
Analyse and identify the linkages and interactions between the component parts of an entire system.
Take ownership of projects, ensuring their successful planning, budgeting, execution, and completion.
Partner with team leadership to ensure collective ownership of quality, timelines, and deliverables.
Develop skills outside your comfort zone, and encourage others to do the same.
Effectively mentor others.
Use the review of work as an opportunity to deepen the expertise of team members.
Address conflicts or issues, engaging in difficult conversations with clients, team members and other stakeholders, escalating where appropriate.
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 team you will lead the design, build, and operation of scalable, secure, and intelligent platforms that enable AI-powered employee and client experiences across the Firm. As a Manager you will combine engineering knowledge with people leadership to deliver resilient platforms that integrate cloud infrastructure, conversational AI, data, and enterprise systems. This role allows you to be at the forefront of technological advancement while mentoring the next generation of engineers and shaping the future of AI solutions.
Responsibilities
- Mentor junior engineers and foster their growth
- Maintain security and scalability of AI-powered solutions
- Collaborate with cross-functional teams to achieve project goals
- Stay at the forefront of technological advancements in AI
What You Must Have
- Bachelor's Degree
- At least 6 years of experience in software, platform, or cloud engineering, including people leadership
- 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
- Master's Degree preferred
- Demonstrating proven experience leading cloud and platform engineering teams
- Offering hands-on experience with Azure, DevOps, and enterprise integration patterns
- Bringing systems thinking to design end-to-end platforms
- Coaching and mentoring engineers to foster a culture of excellence
- Managing delivery plans, dependencies, and risks effectively
- Operating production platforms with a focus on reliability and security
- Working with product owners, UX, data science, and security teams to translate People Tech & AI use cases into scalable platform capabilities
- Defining and enforcing automated testing strategies
- Architecting, building, and deploying conversational bots using Azure Bot Framework SDK and Composer, integrating Azure Cognitive Services (LUIS/Orchestrator) and enterprise channels (Microsoft Teams, Web Chat, Direct Line)
- Leading platform engineering teams delivering cloud-native services and AI-enabled platforms aligned to PwC People Tech & AI strategy

Travel Requirements

Up to 20%

Job Posting End Date

The salary range for this position is: $73,500 - $212,280. For residents of Washington state the salary range for this position is: $73,500 - $244,000. 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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About pwc

Sourced by ZipRecruiter

We know that the future success of our firm is contingent on equitable experiences for our people. From recruitment to partnership, we’re working hard to give every person an equitable opportunity to grow and to thrive as part of our community of solvers. We understand that establishing and maintaining a fair, equitable and welcoming environment for all people requires building a culture of belonging: a shift from awareness to empathy — while demonstrating inclusive leadership that cultivates trust among our people and our clients. PwC is committed to advancing diversity, equity and inclusion (DEI) through an evidence-based strategy designed to achieve well-defined and meaningful aspirational goals. Our aim is to solve problems for the long term, as that is how we build trust and continue to build on our culture of belonging. At the core of this endeavor are stated goals and a series of linked programs enabling targeted interventions at key moments in our employees’ career trajectories.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

London, London, UK