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Machine Learning Engineer Quantization Jobs in West Henrietta, NY

Pwc

US Tech - AI Engineering Manager

Rochester, NY · On-site

$73K - $244K/yr

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 ...

Stellantis

Technology Manager

Hopewell, NY · On-site

This role requires hands-on expertise in programming, systems integration, analytics, and the implementation of cutting-edge technologies such as artificial intelligence, machine learning, and ...

D3 Engineering

Engineering Program Manager

Rochester, NY · On-site

$101.10 - $146.70/hr

Master of Science or PhD in Computer Vision, Machine/Deep Learning, Computer Science, Computer/Electrical/Software Engineering, or a related field. * Experience with autonomous/industrial robotic ...

Innovative Solutions

AI Solutions Engineer

Rochester, NY · Remote

$120K - $150K/yr

Solutions Architect Associate/Professional, Machine Learning Specialty, or Developer Associate (preferred) * Background in healthcare, financial services, or regulated industries with understanding ...

Innovative Solutions

AI Solutions Engineer

Rochester, NY · On-site +1

$120K - $150K/yr

Solutions Architect Associate/Professional, Machine Learning Specialty, or Developer Associate (preferred) * Background in healthcare, financial services, or regulated industries with understanding ...

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Showing results 21-40

All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in West Henrietta, NY

Machine Learning Engineer Quantization information

See West Henrietta, NY salary details

$30.2K

$123.5K

$185.7K

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

As of Aug 13, 2026, the average yearly pay for machine learning engineer quantization in West Henrietta, NY is $123,546.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,400.00 and $148,700.00 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 job categories do people searching Machine Learning Engineer Quantization jobs in West Henrietta, NY look for? The top searched job categories for Machine Learning Engineer Quantization jobs in West Henrietta, NY are:
What cities near West Henrietta, NY are hiring for Machine Learning Engineer Quantization jobs? Cities near West Henrietta, NY with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you

Data Scientist - Analytics as a Service

Ralliant

Fairport, NY • On-site

Full-time

Posted 22 days ago

Job description

Data Scientist - Analytics as a Service
Position Summary
The Senior Data Scientist is responsible for developing the advanced analytics, machine learning models and AI algorithms that power Qualitrol's Analytics as a Service portfolio. Working closely with the Product Owner, Product Engineers and Data Engineer, this individual transforms industrial data into scalable analytics services that deliver measurable customer value.
Unlike a traditional research-oriented data science role, this position is expected to rapidly move algorithms from experimentation into production, continuously improving model performance through customer feedback, operational data and AI-assisted development practices. Success requires balancing scientific rigor with startup execution speed.
Primary Responsibilities
Analytics & Model Development
Develop advanced analytics for:
  • Rotating machine condition monitoring
  • Grid monitoring
  • Predictive maintenance
  • Fault detection
  • Anomaly detection
  • Asset health assessment
  • Failure prediction
  • Fleet benchmarking

Design algorithms that are accurate, explainable and production-ready.
Data Mining & Feature Engineering
Extract insights from:
  • Sensor data
  • Time-series data
  • Event logs
  • Operational history
  • Maintenance records
  • Customer operating conditions

Develop robust feature engineering pipelines to improve model accuracy and scalability.
AI & Machine Learning
Develop and optimize:
  • Machine learning models
  • Statistical models
  • Generative AI applications
  • Large Language Model integrations
  • Predictive analytics
  • Recommendation engines

Leverage AI-assisted tools to accelerate experimentation, model development and validation.
Production Deployment
Partner with Product Engineers to:
  • Deploy models into production
  • Monitor model performance
  • Improve inference accuracy
  • Reduce computational costs
  • Continuously retrain models

Ensure analytics are scalable, reliable and maintainable.
Customer Value Creation
Partner with Product Owner and Customer Success to understand customer use cases and translate them into differentiated analytics capabilities.
Use customer feedback and operational data to continuously improve algorithms and business outcomes.
Required Experience
  • Master's or Ph.D. in Data Science, Computer Science, Statistics, Applied Mathematics or related field
  • 5+ years developing machine learning or industrial analytics solutions
  • Strong Python programming experience
  • Experience with cloud-based ML environments
  • Experience deploying production AI models
  • Strong statistical and analytical skills
Preferred Experience
Experience with:
  • Industrial AI
  • Utilities
  • Rotating machinery
  • Power systems
  • Time-series analytics
  • Azure Machine Learning
  • AWS SageMaker
  • MLOps
  • LLMs and Generative AI
Success Measures
Within 12 months:
  • Multiple production analytics models deployed
  • Measurable improvement in prediction accuracy
  • Repeatable MLOps pipeline established
  • Analytics capabilities contributing to customer adoption
  • Continuous model improvement process operational

#LI-PW1
Ralliant Corporation Overview
Ralliant, originally part of Fortive, now stands as a bold, independent public company driving innovation at the forefront of precision technology. With a global footprint and a legacy of excellence, we empower engineers to bring next-generation breakthroughs to life - faster, smarter, and more reliably. Our high-performance instruments, sensors, and subsystems fuel mission-critical advancements across industries, enabling real-world impact where it matters most. At Ralliant we're building the future, together with those driven to push boundaries, solve complex problems, and leave a lasting mark on the world.
About Qualitrol
QUALITROL manufactures monitoring and protection devices for high value electrical assets and OEM manufacturing companies. Established in 1945, QUALITROL produces thousands of different types of products on demand and customized to meet our individual customers' needs. We are the largest and most trusted global leader for partial discharge monitoring, asset protection equipment and information products across power generation, transmission, and distribution. At Qualitrol, we are redefining condition-based monitoring.
We Are an Equal Opportunity Employer. Ralliant Corporation and all Ralliant Companies are proud to be equal opportunity employers. We value and encourage diversity and solicit applications from all qualified applicants without regard to race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity or expression, or other characteristics protected by law. Ralliant and all Ralliant Companies are also committed to providing reasonable accommodations for applicants with disabilities. Individuals who need a reasonable accommodation because of a disability for any part of the employment application process, please contact us at applyassistance@Ralliant.com.
Pay Range
The salary range for this position (in local currency) is 104300.00-193700.00

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