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Senior Machine Learning Engineer Jobs in Arkansas

Bespoke Labs

Machine Learning Engineer

Conway, AR

* 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 ...

Walmart

Staff, Machine Learning Engineer

Springdale, AR · On-site

$130K - $260K/yr

We are looking for a strong Staff Machine Learning Engineer who has the passion to develop AI driven intelligent products for the Associate Productivity and Experience team, with the ability to ...

Walmart

Staff, Machine Learning Engineer

Bentonville, AR · On-site

$130K - $260K/yr

We are looking for a strong Staff Machine Learning Engineer who has the passion to develop AI driven intelligent products for the Associate Productivity and Experience team, with the ability to ...

Walmart

Staff, Machine Learning Engineer

Centerton, AR · On-site

$130K - $260K/yr

We are looking for a strong Staff Machine Learning Engineer who has the passion to develop AI driven intelligent products for the Associate Productivity and Experience team, with the ability to ...

Walmart

Staff, Machine Learning Engineer

Cave Springs, AR · On-site

$130K - $260K/yr

We are looking for a strong Staff Machine Learning Engineer who has the passion to develop AI driven intelligent products for the Associate Productivity and Experience team, with the ability to ...

Walmart

Staff, Machine Learning Engineer

Elkins, AR · On-site

$130K - $260K/yr

We are looking for a strong Staff Machine Learning Engineer who has the passion to develop AI driven intelligent products for the Associate Productivity and Experience team, with the ability to ...

Walmart

Staff, Machine Learning Engineer

Rogers, AR · On-site

$130K - $260K/yr

We are looking for a strong Staff Machine Learning Engineer who has the passion to develop AI driven intelligent products for the Associate Productivity and Experience team, with the ability to ...

Bespoke Labs

Machine Learning Engineer

Little Rock, AR · 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 ...

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

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All JobsMachine Learning Engineer JobsSenior Machine Learning Engineer JobsSenior Machine Learning Engineer Jobs in Arkansas

Senior Machine Learning Engineer information

See Arkansas salary details

$49.2K

$104.7K

$151.7K

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

As of Sep 4, 2026, the average yearly pay for senior machine learning engineer in Arkansas is $104,650.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,400.00 and $118,700.00 per year, depending on experience, location, and employer.

What does a senior machine learning engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

What are some common challenges senior machine learning engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

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

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

What is the difference between Senior Machine Learning Engineer vs Data Scientist?

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Arkansas?

The most popular types of Machine Learning Engineer jobs in Arkansas are:

What are popular job titles related to Senior Machine Learning Engineer jobs in Arkansas?

For Senior Machine Learning Engineer jobs in Arkansas, the most frequently searched job titles are:

What job categories do people searching Senior Machine Learning Engineer jobs in Arkansas look for?

The top searched job categories for Senior Machine Learning Engineer jobs in Arkansas are:

What cities in Arkansas are hiring for Senior Machine Learning Engineer jobs?

Cities in Arkansas with the most Senior Machine Learning Engineer job openings:

Senior Machine Learning Engineer jobs near you
Infographic showing various Senior Machine Learning Engineer job openings in Arkansas as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $104,650 per year, or $50.3 per hour.

Machine Learning Engineer

Bespoke Labs

Conway, AR

Full-time

Re-posted 19 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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