Top Machine Learning Jobs in Little Rock, AR

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

As a Senior Machine Learning Scientist, you will design, build, and optimize deep learning models for biological data analysis, ensuring high-quality outputs and leading complex projects from conception through deployment.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

As a Senior Machine Learning Engineer, you will develop and deploy ML models for health data analysis, collaborating with cross-functional teams to ensure reliability and regulatory compliance.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

The role involves leading ML client engagements, shaping strategies, advising on ML architecture, and influencing project outcomes, while guiding teams and driving business value.

Lead the development of a robotics data platform, focusing on data enablement, API architecture, and mentoring engineers in complex system designs.

Develop and deploy production-grade AI/ML models, design end-to-end pipelines, and ensure compliance while collaborating with stakeholders in a federal environment.

As an Associate Technical Architect in Machine Learning, you will design and develop advanced ML models on AWS, ensuring scalability and reliability while collaborating with cross-functional teams.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

The AI/ML Engineering Manager leads a team of ML engineers and architects, managing hiring, performance, and customer engagements while providing technical guidance in ML operations and architecture.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

The Machine Learning Engineer II will enhance ML capabilities for autonomous vehicles, train and optimize models, and ensure high performance through collaboration with cross-functional teams.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

Design and implement production-grade ML systems for ICS/OT cybersecurity, build and optimize models (including NLP/LLMs), develop data pipelines and MLOps workflows, collaborate with data teams to deploy and monitor models in cloud and on-prem containerized environments, and troubleshoot production performance and resource issues.

As an Applied Machine Learning Platform Engineer, you'll design and maintain training infrastructure, manage distributed pipelines, and optimize data workflows for machine learning models.

The VP of Research, Machine Learning will set research direction for AI systems, focusing on context representation, reasoning, and evaluation frameworks while ensuring product safety and alignment.

Responsible for building and maintaining end-to-end ML pipelines and deploying production-grade ML systems. Tasks include model fine-tuning, evaluation, and collaboration with engineering teams.

The role involves applying AI/ML techniques to develop model systems for healthcare, collaborating across teams, and conducting R&D.

As a Machine Learning Engineer, you will design and build AI capabilities, develop scalable ML infrastructure, and collaborate on production systems in the real estate domain.

The AI/ML Architect will define scalable architectures, guide teams on MLOps, advise clients on AI solutions, and ensure ethical AI practices.

The role involves developing and optimizing machine learning systems for voice experiences, focusing on speech recognition and language understanding. Responsibilities include training models, ensuring performance metrics, and collaborating with cross-functional teams for successful deployment.

The Lead Machine Learning Engineer will design and evaluate models for autonomous driving systems, mentor junior engineers, and utilize large datasets to improve ML capabilities at May Mobility.

The MLOps Engineer will design and maintain the machine learning operations infrastructure, optimize cloud services, and build CI/CD pipelines to support AI products for underground infrastructure management.