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Deep Learning Quantization Jobs in Chicago, IL (NOW HIRING)

Albi

Senior Machine Learning Engineer (LLMs)

Chicago, IL

$126K - $166K/yr

Deep understanding of transformers, attention, and training dynamics * Strong Python plus PyTorch ... Inference optimization (quantization, speculative decoding, vLLM, Triton) * Experience shipping LLM ...

Albi

Senior Machine Learning Engineer (LLMs)

Chicago, IL · On-site

$126K - $166K/yr

Deep understanding of transformers, attention, and training dynamics * Strong Python plus PyTorch ... Inference optimization (quantization, speculative decoding, vLLM, Triton) * Experience shipping LLM ...

Albi

Senior Machine Learning Engineer (LLMs)

Chicago, IL · On-site

$126K - $166K/yr

Deep understanding of transformers, attention, and training dynamics * Strong Python plus PyTorch ... Inference optimization (quantization, speculative decoding, vLLM, Triton) * Experience shipping LLM ...

Invoca

Senior ML Engineer

Chicago, IL · On-site +1

$107K - $147K/yr

Advanced Python and deep learning proficiency (PyTorch, HuggingFace Transformers, spaCy ... models via quantization, batching, and throughput tuning * Proficiency with inference ...

All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Chicago, IL

Deep Learning Quantization information

See Chicago, IL salary details

$11.3K

$86.4K

$144.2K

How much do deep learning quantization jobs pay per year?

As of Jul 14, 2026, the average yearly pay for deep learning quantization in Chicago, IL is $86,414.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,200.00 and $143,200.00 per year, depending on experience, location, and employer.

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 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 popular job titles related to Deep Learning Quantization jobs in Chicago, IL? For Deep Learning Quantization jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Chicago, IL look for? The top searched job categories for Deep Learning Quantization jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Deep Learning Quantization jobs? Cities near Chicago, IL with the most Deep Learning Quantization job openings:
Deep Learning Quantization jobs near you
AI & Frontier Model Scientist - Phd Fresh Graduate

AI & Frontier Model Scientist - Phd Fresh Graduate

FourKites, Inc.

Chicago, IL • Hybrid

Full-time

Posted 27 days ago

Job description

Get AI-powered advice on this job and more exclusive features.

We are seeking a talented AI Frontier Model Scientist who has recently completed their PhD to join our cutting-edge team. This position is specifically designed for fresh PhD graduates looking to apply their research expertise in a dynamic industry setting. In this role, you'll tackle complex challenges in large language models (LLMs), optical character recognition (OCR), and model scaling. You'll be at the forefront of developing and optimizing AI systems that push the boundaries of what's possible in machine learning.

Key Responsibilities

  • Lead research initiatives to improve OCR accuracy across diverse document types and languages
  • Train and fine-tune LLMs using domain-specific data to enhance performance in specialized contexts
  • Develop techniques to scale LLMs efficiently for high-volume production environments
  • Design and implement novel approaches to model optimization and evaluation
  • Collaborate with cross-functional teams to integrate AI solutions into production systems
  • Stay current with the latest research and incorporate state-of-the-art techniques
  • Document methodologies, experiments, and findings for both technical and non-technical audiences

Required Qualifications

  • PhD in Computer Science, Machine Learning, AI, or a related field (completed within the last year)
  • Strong understanding of deep learning architectures, particularly transformer-based models
  • Experience with OCR systems and techniques for improving text recognition accuracy
  • Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX)
  • Demonstrated ability to implement and adapt research papers into working code
  • Excellent problem-solving skills with a methodical approach to experimentation
  • Strong communication skills to explain complex technical concepts clearly

Preferred Qualifications

  • Research focus during PhD in areas relevant to our work (NLP, computer vision, multimodal learning)
  • Familiarity with distributed training systems for large-scale models
  • Experience with model quantization, pruning, and other efficiency techniques
  • Understanding of evaluation methodologies for assessing model performance
  • Knowledge of MLOps practices and tools for model deployment
  • Publications at top-tier ML conferences (NeurIPS, ICML, ACL, CVPR, etc.)

What We Offer

  • Ideal transition from academic research to industry application
  • Structured onboarding program designed specifically for recent PhD graduates
  • Opportunity to work on frontier AI models with real-world impact
  • Access to significant computing resources for ambitious research
  • Collaborative environment with other top AI researchers and engineers
  • Flexible work arrangements and competitive compensation
  • Support for continued professional development and conference attendance
  • Clear path for growth into senior technical or leadership roles
Seniority level
  • Seniority levelEntry level
Employment type
  • Employment typeFull-time
Job function
  • Job functionResearch and Engineering
  • IndustriesTransportation, Logistics, Supply Chain and Storage

Referrals increase your chances of interviewing at FourKites, Inc. by 2x

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