ZipRecruiter

Log In

1

Deep Learning Quantization Jobs in Missouri (NOW HIRING)

Pwc

AI Engineer

Saint Louis, MO · On-site

$50K - $112K/yr

... networks and deep learning methods for advanced AI applications - Managing data quality and ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

Pwc

AI Engineer

Kansas City, MO · On-site

$50K - $112K/yr

... networks and deep learning methods for advanced AI applications - Managing data quality and ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

Perficient, Inc.

GCP AI Architect

Saint Louis, MO · On-site

Experience goes beyond API calls to include fine-tuning, quantization, and context-window ... Learning. * Google Cloud Ecosystem: Deep familiarity with the GCP AI stack: Vertex AI, BigQuery ...

All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Missouri

Deep Learning Quantization information

What is deep learning quantization?

Deep learning quantization is the process of reducing the precision of the numbers used to represent a neural network's parameters, activations, or both. By converting the typically used 32-bit floating-point values to lower bit-width formats such as 16-bit or 8-bit integers, quantization significantly reduces the memory footprint and computational requirements of deep learning models. This technique helps deploy models efficiently on edge devices and mobile hardware while maintaining acceptable accuracy levels. Quantization is widely used in model optimization for faster inference and lower power consumption.

What are some common challenges faced when implementing deep learning quantization in production environments?

One of the main challenges in implementing deep learning quantization is balancing model accuracy with computational efficiency, as quantization can sometimes lead to a drop in model performance. Additionally, ensuring hardware compatibility and optimizing for different devices (such as CPUs, GPUs, or edge devices) can require extensive testing and tuning. Collaboration with data scientists, software engineers, and hardware specialists is often essential to successfully deploy quantized models at scale. Staying updated with the latest quantization techniques and frameworks is also important for overcoming these challenges.

What are the key skills and qualifications needed to thrive as a deep learning quantization engineer, and why are they important?

To excel as a Deep Learning Quantization Engineer, you need a strong background in machine learning, applied mathematics, and computer science, usually supported by an advanced degree in a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), quantization toolkits, and hardware acceleration platforms is crucial. Analytical thinking, problem-solving, and clear technical communication are standout soft skills in this role. These abilities are essential for efficiently optimizing models for deployment on resource-constrained hardware while maintaining accuracy and performance.

What is the difference between Deep Learning Quantization vs Machine Learning Engineer?

AspectDeep Learning QuantizationMachine Learning Engineer
Required CredentialsAdvanced degrees in AI, Computer Science, or related fields; knowledge of neural networksBachelor's or Master's in CS, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, hardware optimization settingsSoftware development teams, data-driven projects, product-focused environments
Industry UsageAI hardware optimization, model deployment, edge computingModel development, data analysis, software solutions across industries

Deep Learning Quantization focuses on reducing model size and improving inference speed through techniques like weight and activation quantization, often in hardware or embedded systems. Machine Learning Engineers develop, implement, and optimize machine learning models for various applications. While both roles require knowledge of AI and programming, Deep Learning Quantization is more specialized in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.

What are popular job titles related to Deep Learning Quantization jobs in Missouri?

For Deep Learning Quantization jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Deep Learning Quantization jobs in Missouri look for?

The top searched job categories for Deep Learning Quantization jobs in Missouri are:

Deep Learning Quantization jobs near you

Staff Machine Learning Engineer, Generative AI Modeling, Inference

California, MO • On-site

Jobtailor
See more jobs at Jobtailorexternal

$180 - $240/hr

Other

Posted 8 days ago

Job description

  • Develop innovative machine learning technology and products serving millions of Snapchatters
  • Build cutting-edge augmented reality experiences using generative models
  • Deliver generative machine learning experiences on device
  • Partner with cross-functional Snap teams to explore and prototype new products
Requirements
  • Proven passion for machine learning and staying current with research
  • Familiarity with neural networks, deep learning, and generative modeling
  • Deep understanding of mathematics and/or machine learning algorithms
  • Ability to solve open, ambiguous problems
  • Ability to collaborate effectively with internal teams and external partners
  • Ability to work independently
  • Bachelor's degree in a technical field such as computer science, mathematics, or statistics, or equivalent years of experience
  • 8+ years of post-Bachelor’s machine learning or related experience; or a Master’s degree in a technical field plus 7+ years of post-graduate ML or related experience; or a PhD in a related technical field plus 4+ years of post-graduate ML or related experience
  • Experience with computer vision or generative modeling techniques
  • Experience with TensorFlow, PyTorch, JAX, MLX, scikit-learn, or related frameworks
  • Preferred: advanced degree in computer science or related field
  • Preferred: experience training large-scale diffusion models for images, videos, or 3D
  • Preferred: knowledge of distillation, quantization, and model compression techniques
  • Preferred: knowledge of GPU, CPU, or NPU optimization techniques
  • Preferred: experience building and optimizing ML inference pipelines
Core Competencies

Demonstrates expertise in machine learning, particularly in generative modeling and deep learning, with a strong foundation in mathematics and algorithms. Proven ability to collaborate across teams and deliver innovative augmented reality experiences.

Highest-signal resume keywords
  • Machine Learning Expertise
  • Generative Modeling
  • Deep Learning
  • TensorFlow
  • Computer Vision
ATS Optimization Keywords Hard Skills
  • Machine Learning Algorithms
  • Neural Networks
  • Generative Modeling Techniques
  • Mathematics
  • Large-Scale Diffusion Models
  • Model Compression Techniques
  • ML Inference Pipelines
  • GPU Optimization
  • CPU Optimization
  • NPU Optimization
Soft Skills
  • Problem Solving
  • Collaboration
  • Independence
Industry Keywords
  • Augmented Reality
  • Generative Models
  • Cross-Functional Collaboration
  • Research in Machine Learning
Tools & Technologies
  • TensorFlow
  • PyTorch
  • JAX
  • MLX
  • Scikit-learn
#J-18808-Ljbffr

Apply