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Machine Learning Engineer Quantization Jobs in Plano, TX

Samprasoft

Machine Learning Engineer

Roanoke, TX · On-site

Sr. Machine Learning Engineer Duration: 12 -24 Months Location: Merrimack, NH/ Smithfield, RI/ Westlake, TX/ Durham, NC/ Covington, KY/ Jersey City, NJ/ Boston, MA Candidate should be local or ...

Capital One

Senior Machine Learning Engineer

Plano, TX · On-site +1

$100K - $137.30K/yr

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Chase

Lead Machine Learning Engineer-MLOps

Plano, TX · On-site

$98.50K - $129.80K/yr

As Lead Machine Learning Engineer on the Recommendation Engine team, you'll build and maintain ... Implement quantization techniques and deploy large language models (LLMs) to maximize efficiency ...

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site +1

$98.10K - $129.20K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Photon

Machine Learning Engineer - NJ

Dallas, TX · On-site

Machine Learning Engineer We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work ...

SynergisticIT

Machine Learning Engineer

Dallas, TX · On-site

Work with an international top-notch engineering team with full commitment on Machine Learning development. Required Candidate Profile Skills Required * Passionate about search and AI technologies.

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137.30K/yr

Senior Machine Learning Engineer Location: Ann Arbor, Michigan Experience Level: 7+ Years Department: Data Science / Engineering Employment Type: Full-time About the Role: We are looking for an ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100.40K - $137.90K/yr

Senior Machine Learning Engineer Location: Ann Arbor, Michigan Experience Level: 7+ Years Department: Data Science / Engineering Employment Type: Full-time About the Role: We are looking for an ...

Intuit

Staff Machine Learning Engineer

Plano, TX

Come join Intuit as a Staff Machine Learning Engineer! In this role, you'll work alongside AI scientists and machine learning engineers to create AI-powered experiences. You'll be expected to help ...

Intuit

Staff Machine Learning Engineer

Plano, TX

Overview Come join Intuit as a Staff Machine Learning Engineer! In this role, you'll work alongside AI scientists and machine learning engineers to create AI-powered experiences. You'll be expected ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137.30K/yr

We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring ...

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Machine Learning Engineer Quantization information

See Plano, TX salary details

$30.1K

$123.2K

$185.2K

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

As of May 28, 2026, the average yearly pay for machine learning engineer quantization in Plano, TX is $123,243.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,100.00 and $148,300.00 per year, depending on experience, location, and employer.

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 Plano, TX? For Machine Learning Engineer Quantization jobs in Plano, TX, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Plano, TX look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Plano, TX are:
What cities near Plano, TX are hiring for Machine Learning Engineer Quantization jobs? Cities near Plano, TX with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
SR MACHINE LEARNING EMBEDDED ENGINEER

SR MACHINE LEARNING EMBEDDED ENGINEER

Software Technology Inc

Plano, TX • On-site

$119.20K - $156.20K/yr

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Sr Machine Learning Engineer

The client's Mobility team is responsible for building and managing our connected vehicle platforms, supporting product research using vehicle sensor data, and creating new and exciting data services for client-customers that make driving safer, more convenient, and fun. We’re looking for a Sr Machine Learning Engineer capable of using machine learning and statistical techniques to create state-of-the-art solutions for non-trivial, and arguably, unsolved problems. If you are results-driven, interested in how to apply advanced machine learning techniques, would love to work with vehicle telemetry data and video, are deeply technical, highly innovative, and long for the opportunity to build solutions for challenging problems that directly impact the company's bottom-line, we want to talk to you.

Responsibilities
  • Use statistical and machine learning techniques to create scalable solutions for vehicle telemetry data and video analysis, and perform R&D to drive the discovery of new-generation mobility products
  • Establish scalable, efficient, automated processes for large-scale data analysis, model development, model validation and model implementation
  • Develop ML models to run in vehicle (Edge)
  • Develop and deploy CV models on Edge
  • Drive adoption of best practices across organizations
  • Deliver production-ready code
  • Work with Product Owners to define the KPIs for machine learning projects
  • Stay abreast of developments in research methodology and changing technologies in the marketplace and proactively identify applications of these latest developments to improve existing methods
  • Prepare and present findings to both technical and non-technical audiences
  • Work within the constraints of time, budget, and resources capacities to align with the client's global vision
  • Develop and foster collaborative relationships with product, business, and engineering teams to effectively serve our customer needs
Qualifications
  • 5+ years of production experience working in Data Science or Software Engineering
  • 3+ years of production experience in Deep Learning - Computer Vision
  • Solid production experience using Python (including NumPy), C/ C++, Lua and SQL
  • Experience in embedded systems development and troubleshooting and with real-time operating systems
  • Experience with CNNs and other types of neural networks in machine learning, or Robotics, or AI
  • Experience in neural network quantization, compression, and algorithm pruning
  • Application layer development and optimization of deep learning algorithms in embedded systems
  • Experience with C++ development in embedded applications
  • Experience with common embedded operating systems and environments such as Linux, etc.
  • Solid production experience using TensorFlow and/or PyTorch
  • Production experience with Apache Spark
  • Experience implementing solutions for video and image segmentation, object detection and tracking, and/or semantic/instance segmentation
  • Strong fundamentals in problem solving, algorithm design and complexity analysis
  • Experience implementing and orchestrating Machine Learning pipelines in production environments, using tools such as Kubeflow, airflow, Pachyderm, mlflow, etc.

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