Top Machine Learning Jobs in Fayetteville, AR

Serve as primary post-sales strategic partner for largest enterprise clients, driving AI Agent adoption, ROI, optimization, governance, renewals, and cross-functional advocacy to turn customers into champions.

Lead and manage the engineering team to design, build, deploy, and maintain cloud-based social media/influencer marketing and AI platforms. Oversee software architecture, large-scale data pipelines, image/video processing, and AI/ML solutions. Coordinate cross-functional work, hiring, project planning, Agile delivery, and continuous system improvement.

Lead and grow a team of 5-7 enterprise sales reps in the Bay Area, recruit and develop sales leaders, build business plans, drive new-logo acquisition and account expansion, manage the sales cycle from lead generation to close, develop GTM strategy, and report to the VP of Sales. Role supports ambitious quarterly and annual goals and may require occasional work outside standard hours.

Drive sales and revenue growth within a named territory by managing the full sales cycle, building executive relationships, meeting revenue targets, collaborating with solutions and delivery teams, negotiating contracts, managing accounts, representing Cardamom at industry events, and supporting marketing and product improvement efforts.

Own marketing measurement, operations, and analytics: build and QA campaigns, set goals, measure performance and attribution, manage data infrastructure and segmentation, optimize lead scoring/routing, apply AI/ML for automation and predictive insights, and document scalable processes to drive demand generation and cross-team alignment.

Contribute to building and scaling ML development infrastructure for Copilot: design and optimize CI/CD for large-scale training, build developer tools, maintain multi-cloud infrastructure, and deploy/host models for inference and data generation while collaborating with cross-functional teams to improve reliability, performance, and model development workflows.

Lead customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

Lead statistical design and analysis for Phase I-IV clinical studies, develop protocols and SAPs, perform efficacy and exploratory analyses, run simulations, apply advanced methods (mixed models, longitudinal), oversee datasets (ADaM/CDISC), use R and SAS for analyses, and provide strategic statistical guidance to cross-functional teams supporting regulatory submissions.

Drive supply chain efficiency through data analytics, KPI development, process redesign, ERP/WMS/automation selection and implementation, Lean Six Sigma continuous improvement, AI-driven forecasting, cross-functional project leadership, audits, and change management with suppliers and 3PLs.

Lead end-to-end customer implementation projects for an AI-driven scientific platform. Create project plans, manage timelines, risks, and resources, coordinate cross-functional teams, act as primary customer PM contact, and ensure on-time, on-budget delivery and customer satisfaction.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Lead product strategy and execution for a B2B SaaS marketing analytics platform. Translate research into requirements, own the backlog, partner with engineering and GTM, define success metrics, and ship API- and data-heavy features for technical users.

The Senior Director of AI Insights leads AI-driven data insights to enhance decision-making and strategy alignments across the organization, fostering stakeholder engagement and AI literacy.

The role involves cultivating technical relationships with Accenture, supporting solution development, customer deal support, and representing Databricks at events.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Lead and own cross-organizational technical programs for ML and robotics platform development, define roadmaps and milestones, remove blockers, align diverse engineering teams, and drive adoption of platform best practices to deliver scalable autonomy and infrastructure outcomes.

Analyze large federal and state economic datasets, automate data collection and scraping, and rapidly publish clear, data-driven reporting on market-moving indicators. Collaborate with reporters and visual journalists, file public-records requests, and use emerging technologies including AI to enhance real-time coverage.

Sell Litify's AI-driven legal operating platform to law firms and legal departments. Manage full upsell motions, consult on AI adoption, deliver high-level demos to C-suite, meet sales targets, and articulate ROI of autonomous agents and AI integrations.

Lead and mature Omada's enterprise privacy program, manage HIPAA compliance and incident response, embed privacy-by-design across products, conduct privacy risk assessments and vendor due diligence, advise on consumer privacy and AI governance, support interoperability/HIE participation, and deliver privacy training and guidance across the business.

Lead consolidation of customer email and SMS into a centralized CRM (Braze), design and optimize multi-channel customer journeys, manage deliverability and sender reputation, build segmentation and experimentation frameworks, partner cross-functionally to implement integrations and measure campaign performance.

Design and evaluate causal inference experiments and causal ML models to measure product impact, optimize resource allocation, and inform support operations. Build, deploy, and iterate statistical/ML models, analyze tradeoffs, collaborate cross-functionally, and communicate insights to influence decisions and scale data science capabilities.

Build, evaluate, and deploy LLM/ML models and synthetic datasets to improve Community Support. Identify opportunities via data exploration, analyze structured and unstructured data, automate LLM evaluation, personalize experiences, and collaborate cross-functionally to drive product and operational impact.

Own end-to-end product development for AI/ML revenue cycle solutions: define vision and roadmap, write specs and user stories, prioritize backlog, work closely with data scientists and engineers, drive agile delivery, ensure quality, and measure product performance with stakeholders.

Provide legal counsel on privacy, AI, identity, and advertising technology; embed privacy by design with product and engineering teams; draft and negotiate privacy provisions and DPAs; advise on cross-border transfers and SCCs; run privacy and AI risk assessments; support incident response; create scalable legal playbooks and AI-enabled workflows; represent Yahoo in industry standards bodies.