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Machine Learning Engineer Quantization Jobs in Little Rock, AR

Bespoke Labs

Machine Learning Engineer

Conway, AR · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Varsity Tutors

Machine Learning Tutor

Conway, AR · 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 ...

Pwc

AI Engineer

Little Rock, AR · On-site

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

Pwc

AI Engineer

Little Rock, AR

$55K - $187K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ... Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials ...

New

Pwc

AI Solutions Engineering Delivery Lead

Little Rock, AR

$98K - $129K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Westrock Coffee

Sr. Data AI Engineer

Alexander, AR · On-site

$107K - $129K/yr

Strong engineering background, preferably in fields such as Computer Science, Mathematics ... Strong foundation in Machine Learning basics (Evaluation, Training, Problem Decomposition) * In ...

New

Westrock Coffee

Data & AI Engineer

Alexander, AR · On-site

$107K - $129K/yr

Apply machine learning best practices, including feature engineering, model validation, and performance tuning for development and deployment * Utilize Foundry AIP and integrate LLM-based agents to ...

Westrock Coffee

Data & AI Engineer

Alexander, AR · On-site

$107K - $129K/yr

Apply machine learning best practices, including feature engineering, model validation, and performance tuning for development and deployment * Utilize Foundry AIP and integrate LLM-based agents to ...

Pwc

CTIO AI Engineering Manager

Little Rock, AR · On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Pwc

NGA AI Engineer Manager

Little Rock, AR

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Pwc

ERP AI Engineer - Senior Associate

Little Rock, AR · On-site

$77K - $202K/yr

Responsibilities - Design and implement advanced AI and machine learning solutions - Analyze ... Engineering, Mathematics, Statistics, or a related quantitative field - At least 3 years of ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Little Rock, AR

Machine Learning Engineer Quantization information

See Little Rock, AR salary details

$30.2K

$123.5K

$185.6K

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

As of Jul 31, 2026, the average yearly pay for machine learning engineer quantization in Little Rock, AR is $123,508.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,400.00 and $148,700.00 per year, depending on experience, location, and employer.

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 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.

Machine Learning Engineer Quantization jobs near you

Machine Learning Engineer

Bespoke Labs

Conway, AR • On-site

Full-time

Re-posted 14 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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