Top Senior Machine Learning Jobs in Raleigh, NC

Design, develop, and operate scalable software platforms and automation for Azure network qualification infrastructure. Build tools for device provisioning, configuration, lifecycle management, testing, telemetry, and health validation. Develop AI-powered agents for troubleshooting, root-cause analysis, and engineering productivity. Create reliable distributed systems with observability, diagnostics, automated recovery, and scalability while collaborating across software and network engineering teams.

Develop and implement agentic and generative AI solutions, including LLMs, RAG, AI security, and related platforms. Support research and development, technical proposals, prototyping, and client consulting. Apply data science and engineering best practices, integrate and analyze data, gather requirements, and translate technical concepts for clients and stakeholders.

Lead technical strategy for financial services customers by designing scalable data, AI, streaming, and analytics architectures. Own customer engagements from discovery through production deployment, advise senior technical stakeholders, drive Databricks adoption, lead competitive technical wins, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL coding, distributed data systems expertise, public cloud deployment experience, and the ability to translate complex architectures into business value.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Lead technical onboarding and enablement for contact center partners, architecting scalable Zendesk, Amazon Connect, and AWS solutions. Provide co-selling support, technical strategy, solution design, demonstrations, proof-of-concepts, training, documentation, governance, and mentorship. Collaborate with partners, sales, engineering, and vendors to drive successful implementations, security compliance, innovation, and adoption of AI, automation, and omnichannel contact center technologies.

Own the end-to-end product strategy and lifecycle for AI research products, from model development and user research through launch, pricing, and go-to-market. Partner with research, engineering, design, and business teams to define requirements, ship products, analyze metrics, run experiments, and prioritize features. The role requires strong technical or quantitative foundations, product management experience, SQL and Python fluency, knowledge of machine learning, and experience taking products from zero to one.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead end-to-end technical strategy for customer accounts, design scalable data and AI architectures, and drive Databricks adoption and consumption. Advise senior technical stakeholders, lead architecture discussions, build custom solutions for competitive opportunities, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL skills, distributed data systems expertise, public-cloud deployment experience, and the ability to translate technical concepts into business value.

Lead ecosystem-level data science and econometric analyses across Xbox, Game Pass, Console, PC, and Store experiences. Evaluate growth, engagement, monetization, experiments, incrementality, and investment ROI using statistical and causal inference methods. Build reusable analytical products and self-service capabilities, influence executive decisions, and mentor team members.

Leads Kaseya’s market and competitive intelligence function, including competitor analysis, market sizing, customer segmentation, Voice of Customer, win/loss research, marketing effectiveness, and predictive analytics. Advises executives on market trends, customer behavior, competitive threats, and growth opportunities. Partners with product, sales, marketing, data science, and revenue operations, embeds insights into sales enablement, and builds a high-performing intelligence team.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes trusted metrics, partners with Finance, Product, Commercial, Engineering, and Data Engineering, and mentors scientists. The role drives adoption of governed self-service analytics and communicates assumptions, uncertainty, and business trade-offs to leadership.

Lead architecture and technical strategy for a remote engineering organization building a React, Node.js, PostgreSQL, and AWS platform. Mentor engineers, establish engineering standards, drive JavaScript-to-TypeScript migration, design ETL and AI/LLM integration systems, resolve technical debt, and oversee technical design reviews, production support, incident response, hiring, and delivery. Partner with product, clinical, and business stakeholders to build scalable, reliable software.

Lead technical discovery and solution design for U.S. Government customers adopting trusted AI platforms. Develop architectures, demonstrations, prototypes, and proof-of-concepts; translate mission needs into actionable implementation approaches; present to technical and executive stakeholders; collaborate with engineering, data science, and product teams; support sales, proposals, and capture efforts; and manage concurrent customer engagements.

Manage strategic state and local government accounts for NVIDIA’s AI and accelerated computing solutions. Own the full technology sales cycle, develop territory and account plans, build executive and technical relationships, collaborate with solution architects and channel partners, navigate public-sector procurement, forecast revenue, and achieve sales quotas. The role requires consultative selling to government organizations and travel of at least 25%.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Own projects from prototyping through production, conduct experiments, explore large-scale data, and collaborate with product, engineering, analysts, and account teams to improve attribution and marketing ROI.

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Secure software and cloud environments through threat modeling, code review, vulnerability remediation, security automation, detection engineering, and attack simulations. The role partners closely with Engineering, manages Snyk and Docker Scout findings, integrates security into CI/CD, coordinates third-party penetration testing for SOC 2 and HITRUST, and helps define secure architecture for mobile, data lake, AI, and production cloud systems.

Manage technology operations across three global studios, including systems administration, infrastructure, technical support, equipment management, business and creative tool administration, vendor coordination, and troubleshooting. Partner with global IT, information security, and privacy teams on policies, compliance, and content security. Drive AI and machine-learning integrations to improve workflows and business efficiency while supporting clients and global technology initiatives.

Own and advance Wonderlic’s AI-powered Jobs Engine, using production machine learning and NLP to analyze labor-market data and infer job characteristics at scale. Partner with product, engineering, and I-O psychology teams to integrate responsible AI into assessment and workforce solutions. Apply fairness, explainability, legal defensibility, and rigorous evaluation practices to employment-related AI systems, including generative AI outputs and assessment interpretation.

Leads client-facing management consulting and transformation programs spanning revenue operations, marketing, sales, customer growth, data, analytics, and AI. Advises senior executives, develops operating models and transformation roadmaps, leads solutioning and proposals, oversees delivery governance, and translates business needs into implementable data and technology solutions. Builds thought leadership, reusable consulting assets, and high-performing teams while supporting strategic growth and client relationships.

Leads and develops a fraud analyst team responsible for investigating emerging threats, responding to incidents, and improving fraud-risk strategies. Owns investigative standards, playbooks, workflows, and tools while serving as an escalation resource for complex cases. Partners with Data Science, Customer Success, Product, Engineering, and business leaders to convert fraud insights into scalable signals, model improvements, and better customer outcomes. Supports customer conversations as a fraud subject-matter expert and coaches analysts on communicating risk decisions.

Design, scale, and optimize ClickUp's search infrastructure using OpenSearch/Elasticsearch. Improve relevance, speed, and real-time indexing, build vector search capabilities, create evaluation frameworks, and collaborate with AI/backend/product teams to integrate and troubleshoot search at scale.