Top Machine Learning Jobs in Fayetteville, AR

Own end-to-end design for complex financial product experiences from exploration through launch and iteration. Translate financial workflows into intuitive customer experiences, set high standards for interaction and visual design, partner with product and engineering, use research and data to guide decisions, contribute to design systems, present work clearly, and mentor teammates. Apply AI-enabled tools thoughtfully while maintaining strong human-centered judgment.

Lead implementations of iQueue for Operating Rooms from discovery through configuration, validation, and rollout. Partner with hospital, clinical, operational, IT, and executive stakeholders to optimize OR workflows, scheduling, staffing, utilization, and access. Responsibilities include data analysis, solution design, product configuration, user acceptance testing, change management, risk monitoring, stakeholder alignment, and go-live readiness. Travel to customer sites is required up to 40–50%.

Lead initiatives in designing and building large-scale distributed data systems and ML infrastructure, while collaborating across various engineering teams.

Leads an engineering team building and scaling customer-facing integrations for agentic AI products. Responsibilities include setting technical direction, balancing customer delivery with reusable tooling and platform investments, ensuring reliability and observability, improving developer experience, collaborating cross-functionally, recruiting, and coaching engineers. The role requires customer empathy, strong engineering leadership, technical problem-solving, and experience with production systems or integrations.

Assess AI-enabled products and software against regulatory requirements and standards, supporting conformity assessments, CE marking, certification, and client guidance. Review technical documentation, mentor colleagues, develop assessment procedures and tools, contribute to AI testing methodologies, and support accreditation, quality, and continuous improvement activities. The role requires practical AI/ML experience, regulatory knowledge, strong analytical skills, and collaboration across global multidisciplinary teams.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Build and operate scalable, highly available ML/LLM serving and compute infrastructure. Own long-term platform vision, deliver zero-to-one projects, partner cross-functionally, optimize performance and capacity, and support production ML models at scale.

Design, productionize, and deploy machine learning forecasting models for ad inventory and campaign performance in low-latency, real-time advertising environments. Build scalable simulation, budgeting, and pacing systems; collaborate with cross-functional teams to deploy models at scale and optimize ad monetization.

Lead design, execution, and optimization of scaled lifecycle campaigns across email and in-product channels. Use marketing automation, behavioral data, and ML techniques to personalize journeys, drive activation, revenue, retention, and cross-sell. Analyze performance, generate insights, and collaborate with Growth, Product, Engineering, Data Science, Analytics, Sales, and Marketing stakeholders.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Architect and implement enterprise-grade MLOps/LLMOps platforms on AWS and Kubernetes. Design ML/LLM pipelines for training, validation, deployment, monitoring, governance, CI/CD, and observability. Serve as technical authority, conduct architecture reviews, ensure security and compliance, and mentor engineering teams across projects.

Lead the hands-on development of Vida’s secure data and AI/ML platform, including ingestion, data modeling, MLOps, model serving, feature pipelines, LLM integrations, DevOps, observability, and infrastructure. Partner with security and IT to protect sensitive health data and meet HIPAA/HITRUST-aligned controls while establishing scalable engineering patterns across the organization.

Lead the architecture, implementation, governance, and operation of enterprise AI agents using Amazon Bedrock AgentCore. Design agents, knowledge bases, action groups, orchestration workflows, registries, guardrails, and emergency shutdown controls. Guide ECS deployments, API Gateway integrations, IAM governance, authentication, and platform standardization. Provide technical leadership to engineering and DevOps teams while collaborating with AWS specialists, customers, and business stakeholders.

Conduct machine learning research operations using existing infrastructure. Characterize neural network quality, failure modes, and edge cases; coordinate experiments with researchers; manage concurrent experiments and deadlines; review results; recommend theoretical or process improvements; and prepare technical reports communicating qualitative and quantitative findings.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

The Enterprise Account Executive drives revenue through high-value contracts, managing complex sales processes and relationships with executive stakeholders in financial services.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.

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.

Build and ship production LLM-powered agents, workflows, and integrations using platform templates, pipelines, and guardrails. Develop comprehensive evaluation suites, integrate business systems and data sources, instrument cost and quality telemetry, and collaborate with business users to iterate within defined scopes. Requires extensive software engineering experience, production LLM application expertise, strong Python and TypeScript skills, API craftsmanship, prompt and context engineering, and a commitment to guardrails and evaluation.

Build and deploy LLM-powered agents and workflows using production templates, pipelines, and guardrails. Develop comprehensive evaluations, integrate AI systems with business data sources, and instrument cost and quality telemetry. Collaborate with business users to rapidly iterate within defined scopes while maintaining production standards. The role requires extensive software engineering experience, production LLM application development, strong Python and TypeScript skills, API expertise, and retrieval or healthcare data experience as desirable qualifications.

Build and deploy LLM-powered agents, workflows, and integrations using production templates, pipelines, and guardrails. Develop comprehensive evaluation suites, integrate with business systems and data sources, and instrument solutions for cost and quality telemetry. Collaborate with business users to iterate rapidly within defined scopes. The role requires strong Python and TypeScript skills, production LLM application experience, API development, prompt and context engineering, and retrieval expertise.

Build and deploy LLM-powered agents and workflows using production templates, pipelines, and guardrails. Develop comprehensive evaluations, integrate with business systems and data sources, instrument cost and quality telemetry, and collaborate with business users to iterate within defined scopes. The role requires strong Python and TypeScript skills, production experience with agents, RAG, and tool use, as well as expertise in prompt and context engineering.

Develops AI/ML prototypes, APIs, microservices, integrations, and lightweight user interfaces for federal and DoD-aligned mission workflows. The role integrates and optimizes machine learning models for dashboards and applications, supports data ingestion and preprocessing, validates performance, and helps transition models into production. Collaboration with data scientists, architects, developers, PMO leadership, and cybersecurity teams is required. The position requires five years of relevant experience, a bachelor’s degree, ITIL v4 Foundation certification, and Public Trust or active Secret clearance.