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Machine Learning Engineer Quantization Jobs in Sarasota, FL

Varsity Tutors

Machine Learning Tutor

Saint Petersburg, FL · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Paychex

Data Solutions Engineer

Saint Petersburg, FL · On-site +1

$91K - $156K/yr

Stay abreast of the latest trends in cloud computing, machine learning, AI, and data engineering. Explore new technologies and methodologies to continuously improve systems, tools, and data processes.

LaBine and Associates

DSP Engineer

Sarasota, FL · On-site

$138K - $161K/yr

We are seeking an experienced engineer with a strong academic background in engineering/science ... Digital Signal Processing, X-Midas, Linux, and Machine Learning experience preferred. * TS/SCI ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Sarasota, FL

Machine Learning Engineer Quantization information

See Sarasota, FL salary details

$30.4K

$124.1K

$186.5K

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

As of Jul 4, 2026, the average yearly pay for machine learning engineer quantization in Sarasota, FL is $124,098.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,800.00 and $149,400.00 per year, depending on experience, location, and employer.

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 Sarasota, FL? For Machine Learning Engineer Quantization jobs in Sarasota, FL, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Sarasota, FL look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Sarasota, FL are:
What cities near Sarasota, FL are hiring for Machine Learning Engineer Quantization jobs? Cities near Sarasota, FL with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
Infographic showing various Machine Learning Engineer Quantization job openings in Sarasota, FL as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $124,098 per year, or $59.7 per hour.
Machine Learning Engineer

Machine Learning Engineer

Roper Technologies

Sarasota, FL • On-site

Full-time

Posted 26 days ago

Roper Technologies rating

8.2

Company rating: 8.2 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

83rd of 202 rated software companies

Job description

Roper Technologies is seeking a Machine Learning Engineer to help design, build, and deploy advanced AI systems across our portfolio of market-leading software businesses. 
This role will focus on developing scalable machine learning products and services, shared AI components, and intelligent agents that drive meaningful business impact. Depending on experience level, the role may involve leading architectural initiatives, mentoring engineers, and shaping technical strategy. 
 
We are looking for hands-on engineers who are excited about building production-grade AI systems—not just prototypes—and who thrive in a high-impact, applied environment. Candidates who have demonstrated ability to think through product as well as engineering are highly desired.
 
What You’ll Do 
 
AI & ML System Development 
  • Design, build, and deploy machine learning models and AI systems in production environments 
  • Develop components such as:
    • Model inference services 
    • Data and feature pipelines
    • Complex recommendation and matching services
    • Vision based analysis systems
    • Evaluation and monitoring pipelines 
  • Optimize models for performance, reliability, and cost efficiency      
Intelligent Agents & Applied AI 
  • Contribute to the development of AI agents and multi-step workflow automation systems 
  • Build systems that integrate with enterprise tools and APIs 
  • Implement tool-use frameworks, memory mechanisms, and evaluation loops 
  • Experiment with LLMs, foundation models, and fine-tuning approaches 
  • Help translate AI research advances into practical, scalable solutions 
Engineering Excellence 
  • Write high-quality, maintainable, and well-tested code 
  • Participate in architecture design and technical reviews 
  • Contribute to CI/CD pipelines and MLOps workflows 
  • Implement observability and monitoring for AI systems in production 
  • Follow security, compliance, and responsible AI best practices 
Cross-Functional Collaboration 
  • Partner with product, data engineering, and infrastructure teams 
  • Help identify high-impact AI use cases within portfolio companies 
  • Support integration of shared AI components into business applications 
  • Communicate technical tradeoffs clearly to both technical and non-technical stakeholders 
Qualifications 
 
We welcome candidates across a range of experience levels. The scope and seniority of responsibilities will scale accordingly.  
Required 
  • 3+ years of experience in software engineering, data science, or machine learning (more for senior roles) 
  • Experience building and deploying production software systems 
  • Strong programming skills in Python (experience in additional languages is a plus) 
  • Familiarity with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) 
  • Understanding of modern AI architectures, including LLM-based systems 
  • Experience working with cloud environments (AWS, Azure, or GCP) 
  • Strong problem-solving skills and attention to detail 
 
Preferred  
  • Experience with:
    • Fine tuning, experimentation, etc.
    • Rapid development using AI tools 
    • Agent frameworks and orchestration tools 
    • Distributed systems or microservices architecture 
    • Model monitoring and evaluation frameworks 
  • Experience building reusable libraries or shared infrastructure 
  • Exposure to SaaS products or enterprise software environments 
  • Background in optimizing models for performance and cost
Leveling & Growth 
 
We are hiring across multiple experience levels: 
  • Intermediate ML Engineer – Contributes independently to projects, builds production features, collaborates cross-functionally. 
  • Senior ML Engineer – Owns complex systems end-to-end, drives architectural decisions, mentors others. 
  • Principal / Staff ML Engineer – Defines technical direction, leads cross-portfolio initiatives, designs shared frameworks and scalable AI infrastructure. 
Level and compensation will be determined based on experience and demonstrated expertise. 
 
What We Value 
  • Strong engineering fundamentals 
  • Practical, impact-driven AI development 
  • Curiosity and willingness to experiment responsibly 
  • Ownership mindset and bias toward execution 
  • Ability to balance innovation with reliability 
Why Join Roper 
  • Work on high-impact AI systems across a diverse portfolio of leading software businesses 
  • Build reusable infrastructure that scales across industries 
  • Collaborate with experienced engineering and executive leadership 
  • Shape the next generation of intelligent enterprise software 

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