Top Machine Learning Jobs in Fayetteville, AR

The role involves improving AI agent quality through designing experiments, maintaining evaluation infrastructure, optimizing LLM inference, and collaborating with product teams. Responsibilities include end-to-end ownership from research to deployment in a dedicated ML Engineer context.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

Design, build, and productionize scalable AI systems for autonomous detection, RAG-backed investigations, and auto-remediation. Develop LLMs, graph-based reasoning engines, streaming pipelines, evaluation/red-team processes, and collaborate with cross-functional teams to translate attacker behavior into robust detections.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

Deploy, optimize, and scale ML models for datacenter and edge AV devices; own model compilation and deployment pipelines; profile and enforce latency/throughput budgets, and apply quantization, distillation, and pruning to meet onboard compute constraints.

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.

The Lead Machine Learning Scientist will develop and implement ML strategies to enhance marketing and product capabilities, identify opportunities for growth using data, lead core model development, and improve marketing spend efficiency through insights and experimentation.

Design, research, and deliver LLM and GenAI solutions for healthcare SaaS at scale: architect systems, handle healthcare data, implement PEFT and RAG pipelines, ensure responsible AI, mentor teams, and translate technical outcomes to business impact.

Design, build, and deploy production ML systems for demand forecasting, labor optimization, and LLM-powered workflows. Own end-to-end ML lifecycle: data ingestion, feature engineering, model training, deployment, monitoring, drift detection, evaluation, and experimentation. Collaborate with Product, Engineering, and Operations to integrate models into core workflows and translate performance into business insights.

Design, build, and deploy scalable ML models and pipelines to enhance a talent marketplace and GenAI solutions. Lead experimentation (A/B tests, statistical validation), improve production ML performance, ensure code and data quality, work with cross-functional teams, mentor junior engineers, and propose innovative AI-driven solutions aligned to business goals.

Design, build, and deploy production-grade AI/ML and LLM-based systems. Lead end-to-end ML lifecycle, implement RAG and embedding pipelines, establish MLOps best practices, mentor engineers, and collaborate with cross-functional teams to deliver scalable, responsible AI solutions.

Lead and scale a global Data & ML practice, define data and ML strategy, architect cloud-native data platforms, oversee MLOps/DataOps and CI/CD, support pre-sales and solution design, hire and mentor distributed teams, and translate technical capabilities into measurable business outcomes for enterprise clients in a remote consulting environment.

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

As a Senior Machine Learning Engineer, you will design and maintain ML systems for content recommendations, search, and spam detection, while augmenting user experience and system performance.

Manage and mentor a team of machine learning and data engineers, overseeing ML model development, data pipelines, and collaborating with product teams on technical requirements.

Drive delivery excellence and customer success by leading the architecture and implementation of AI/ML solutions while ensuring account growth and client engagement.

The Expert Product Architect AI/ML will design technical foundations for Managed AI Services, ensuring scalable and secure integrations of AI capabilities and operational frameworks within Ensono's offerings.

The AI/Machine Learning Engineer will develop systems for automating health benefits decisions, integrating large language models with rule-driven patterns, and ensuring compliance with regulations.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

The Staff Software Engineer will build and scale machine learning systems for personalization, collaborating with teams to enhance project quality and performance.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.