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Machine Learning Engineer Quantization Jobs in Black Earth, WI

Bespoke Labs

Machine Learning Engineer

Madison, WI · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Madison, WI

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Varsity Tutors

Machine Learning Tutor

Madison, WI · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Abbott Laboratories

Sr. Engineer, AI Platform

Madison, WI · On-site

$125 - $150/hr

Position Overview The Lead Engineer, Artificial Intelligence is a pivotal position responsible for ... This role demands expertise in Machine Learning, Natural Language Processing, and emerging ...

Abbott

Sr. Engineer, AI Platform

Madison, WI

$99K - $198K/yr

Position Overview The Lead Engineer, Artificial Intelligence is a pivotal position responsible for ... This role demands expertise in Machine Learning, Natural Language Processing, and emerging ...

Abbott

Sr. Engineer, AI Platform

Madison, WI · On-site

$99K - $198K/yr

Position Overview The Lead Engineer, Artificial Intelligence is a pivotal position responsible for ... This role demands expertise in Machine Learning, Natural Language Processing, and emerging ...

Abbott

Sr. Engineer, AI Platform

Madison, WI

$99K - $198K/yr

Position Overview The Lead Engineer, Artificial Intelligence is a pivotal position responsible for ... This role demands expertise in Machine Learning, Natural Language Processing, and emerging ...

Insperity

Artificial Intelligence Engineer III

Madison, WI · On-site

$58 - $77.75/hr

This role applies advanced software, data science, machine learning, and LLM engineering expertise to build AI powered applications, model driven solutions, and intelligently automated workflows. The ...

Insperity

Artificial Intelligence Engineer III

Madison, WI · On-site

$58 - $77.75/hr

This role applies advanced software, data science, machine learning, and LLM engineering expertise to build AI powered applications, model driven solutions, and intelligently automated workflows. The ...

Abbott

AI Platform Engineer

Madison, WI · On-site

Position Overview The AI Platform Engineer builds and operates the machine learning and generative ... such as quantization, batching, or graph compilation (for example ONNX Runtime or TensorRT)

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Black Earth, WI

Machine Learning Engineer Quantization information

See Black Earth, WI salary details

$32.2K

$131.8K

$198K

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

As of Sep 9, 2026, the average yearly pay for machine learning engineer quantization in Black Earth, WI is $131,796.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,900.00 and $158,600.00 per year, depending on experience, location, and employer.

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 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 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 cities near Black Earth, WI are hiring for Machine Learning Engineer Quantization jobs?

Cities near Black Earth, WI with the most Machine Learning Engineer Quantization job openings:

Machine Learning Engineer Quantization jobs near you
Infographic showing various Machine Learning Engineer Quantization job openings in Black Earth, WI as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 22% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $131,796 per year, or $63.4 per hour.

Machine Learning Engineer

Madison, WI • On-site

Bespoke Labs
See more jobs at Bespoke Labsexternal

Full-time

Re-posted 23 days ago

Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems

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