Top Machine Learning Jobs in Fayetteville, AR

Lead development of safety frameworks and V&V for end-to-end autonomous robotics, perform risk assessments and safety reviews, derive technical safety requirements, and coordinate cross-functional teams to monitor fleet performance and deploy scalable autonomy.

As a Research/ML Engineer, you will translate cutting-edge AI research into product solutions, develop safety and reliability systems, and collaborate with technical teams.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

The Research Advisor will provide high-level consultancy for generative AI systems, engage in research collaborations, and represent the Human Frontier Collective in key executive meetings.

The Director of AI will lead AI and machine learning initiatives, develop AI architecture for humanoid robots, oversee production-ready AI systems, and manage team growth and budget.

Lead a team to enhance Cengage Learning Platforms by implementing AI-first strategies, improving software delivery processes, and ensuring product quality.

Lead the architecture, development, modernization, performance optimization, and reliability of enterprise-scale machine learning platform software. Mentor engineers, influence cross-team technical decisions, conduct code reviews, evaluate AI-assisted code, design cloud-native systems, and drive high-quality delivery across complex application layers. The role requires expertise in distributed systems, cloud infrastructure, production services, and enterprise engineering practices.

Design and deliver enterprise-grade generative AI solutions on AWS using Bedrock, AgentCore, SageMaker, LLMs, RAG pipelines, vector databases, and agentic workflows. Build scalable APIs and integrations, optimize prompts and models, evaluate performance, and implement security and governance. Collaborate with application, data, and platform teams, troubleshoot production systems, define best practices, and mentor engineers while remaining hands-on.

Develop and expand software products and platforms across backend, frontend, cloud, AI, and machine learning systems. Build efficient, reliable, and testable code; collaborate with business partners to launch features; influence product and engineering decisions; mentor team members; and help shape team culture and product vision in an agile environment.

Design, build, evaluate, and deploy AI agents and machine learning automation for engineering, product, delivery, and customer-facing workflows. Integrate AI into existing software, collaborate with technical and healthcare stakeholders, optimize systems for accuracy, latency, cost, and safety, and document development processes. The role emphasizes LLMs, agentic frameworks, production ML systems, human-in-the-loop safeguards, and healthcare-aware solutions.

Develop ML/RL models and infrastructure for AV simulations, optimize training loops, and support project timelines in autonomous vehicle tech.

Define and lead enterprise-wide AI/ML architecture strategy, standards, governance, reference architectures, and adoption roadmaps. Design scalable architectures for generative AI, LLM applications, RAG, AI agents, MLOps, data platforms, cloud infrastructure, security, and enterprise applications. Lead technology evaluations, architecture reviews, modernization strategies, responsible AI controls, vendor decisions, and transformation programs while advising executives and technical teams.

Own product strategy, vision, roadmaps, backlogs, and delivery for enterprise SaaS products serving financial services organizations. Translate strategy into requirements and user stories, lead Agile ceremonies, engage customers, monitor product metrics, manage delivery risks, and align portfolio companies with leadership and cross-functional teams. Identify and deliver AI-enabled capabilities, workflow automation, analytics, and intelligent business solutions.

Full-cycle quota-carrying Account Executive selling Bold Metrics' SaaS fit and sizing solutions to apparel brands. Own outreach through contracting, build ROI cases, manage cross-functional coordination, develop outbound strategies, leverage AI to scale workflows, track funnel metrics, and feed customer insights to Product.

Lead and mentor full-stack engineering teams to design, build, test, deploy, and support scalable client-facing and proprietary software. Drive architecture, technical roadmap, Agile delivery, cloud-native migrations, API integrations, quality metrics, and cross-functional collaboration while balancing hands-on development and people leadership to meet product and business goals.

Serve as the primary product contact embedded with health system clients to discover workflows, design AI-powered solutions, scope pilots, and lead 0→1 deployments. Translate clinical and administrative workflows into product requirements, drive UAT and go-live, measure business success metrics, and surface field insights into the product roadmap. Travel to client sites for discovery and deployment support and collaborate with ML engineers, FDEs, and solution teams to ensure measurable ROI and expansion.

Build and operate scalable ML infrastructure for data preparation, model training, evaluation, deployment, monitoring, and rollback. Develop AWS and Kubernetes platforms, reproducible pipelines, dataset and model versioning, observability, testing, security controls, and self-service tooling. Partner with autonomy, software, data, simulation, and security teams to support cloud-to-edge ML workflows and reliable production deployments.

Build and productionize customer-facing data and AI solutions on the Databricks platform. Own architecture and design decisions, develop end-to-end data pipelines and applications, integrate machine learning models, and guide enterprise customers through implementation and adoption. Collaborate with engineering, product, support, project managers, and customer stakeholders while delivering projects, documenting solutions, resolving issues, and creating reusable frameworks. Travel to customers approximately 20% of the time.

Owns a portfolio of NovaOS capabilities from concept through delivery, adoption, and continuous improvement. Responsibilities include managing product backlogs and roadmaps, prioritizing features, defining requirements and success metrics, coordinating cross-functional engineering, AI, UX, data, and architecture teams, leading product discovery and release planning, monitoring adoption and KPIs, and ensuring alignment with product governance, architecture, and Responsible AI standards.

Leads the strategy, roadmap, and product organization for NovaOS, an enterprise AI operating system. Oversees a portfolio spanning data, semantic, agentic, and experience layers; partners with business, engineering, and technology leaders to prioritize investments and deliver measurable outcomes. Establishes product governance, lifecycle practices, success metrics, and feedback mechanisms while building and mentoring a high-performing product management team.

Leads LaunchDarkly’s global partnerships and alliances strategy across GSIs, SIs, cloud, VAR, and technology partners. Builds the partnerships organization, partner programs, co-sell motions, services offerings, and PRM ecosystem. Owns partner-sourced pipeline, influenced revenue, budgets, forecasting, executive relationships, enablement, and cross-functional collaboration across Sales, Product, Services, Marketing, Finance, Legal, and Customer Success.

Leads enterprise AI-focused sales campaigns across a regional territory, advising C-suite and technical AI buyers on data platform and infrastructure solutions. Partners with account executives, solution architects, engineers, product teams, GSIs, and channel partners to develop use cases, reference architectures, ROI frameworks, and joint go-to-market strategies. Owns pipeline discipline, forecasting, account planning, complex deal orchestration, and AI solution positioning for enterprise workloads.