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Deep Learning Quantization Jobs in Lansing, 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 ...

Pwc

AI Engineer

Chicago, IL · 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 ...

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

Deep Learning Quantization information

See Lansing, IL salary details

$10.7K

$81.9K

$136.6K

How much do deep learning quantization jobs pay per year?

As of Aug 6, 2026, the average yearly pay for deep learning quantization in Lansing, IL is $81,863.00, according to ZipRecruiter salary data. Most workers in this role earn between $70,300.00 and $135,600.00 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 Lansing, IL? For Deep Learning Quantization jobs in Lansing, IL, the most frequently searched job titles are:
What cities near Lansing, IL are hiring for Deep Learning Quantization jobs? Cities near Lansing, IL with the most Deep Learning Quantization job openings:
Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in Lansing, IL as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $81,863 per year, or $39.4 per hour.

Senior Machine Learning Engineer (LLMs)

Albi

Chicago, IL

$126K - $166K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted yesterday

Job description

We're building deeply integrated LLMs into a real product used daily by restoration companies running thousands of jobs. This is not a "prompt engineer" role. You'll design, train, and ship domain-specific language models that automate real workflows and move real revenue.

You will:

  • Own endtoend LLM systems: architecture, training, evals, and iteration
  • Finetune and extend existing models (LoRA, instruction tuning, RLHF)
  • Build and maintain data pipelines from product databases, documents, APIs, and logs
  • Ship reliable, monitored, production models with clear guardrails
  • Collaborate closely with product and engineering to turn messy realworld problems into working systems
  • Build and coordinate the AI engineering team
  • Use Claude Code as a core tool for development, refactors, tests, and experiments

This is for you if:

  • "How does this actually work under the hood?" is your default question
  • You're fine sitting with a hard problem for days and reading papers on weekends to figure it out
  • If there's something interesting to learn or solve, it doesn't matter if it's Saturday or 1 a.m., you're in
  • You build side projects nobody asked for and write cleaner code than anyone requires
  • You're quietly competitive, selftaught in at least one major skill, and think in systems
  • You're slightly allergic to meetings without a clear purpose or owner

Requirements

  • 5+ years of real world experience in ML / AI engineering
  • Proven experience training or substantially contributing to training LLMs (not just calling APIs)
  • Deep understanding of transformers, attention, and training dynamics
  • Strong Python plus PyTorch or JAX
  • Experience with largescale data pipelines and experiment tracking
  • Handson finetuning (LoRA, instruction / SFT, RLHF or similar)
  • Comfortable using Claude Code as part of your daily workflow
  • Able to explain complex systems simply to nontechnical stakeholders and go deep with experts
  • Track record of owning projects endtoend and mentoring other engineers

Nice to have:

  • Distributed training (FSDP, DeepSpeed, Megatron, etc.)
  • Inference optimization (quantization, speculative decoding, vLLM, Triton)
  • Experience shipping LLM features in production SaaS
  • Opensource contributions or published work or patents in ML / NLP
  • Microsoft Foundry experience

Benefits

  • Competitive salary (based on experience and location)
  • Generous PTO
  • Medical, dental, and vision coverage
  • 401(k) plan
  • High ownership and autonomy over your work
  • Direct collaboration with a small team of smart, kind, motivated engineers
  • An environment that values deep work, clear thinking, and real impact
  • Regular team events and offsites
  • Equipment and learning budget to help you do your best work and keep up with the frontier

Apply