Top Machine Learning Jobs in Raleigh, NC

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

Develop new enterprise and upper mid-market business, manage client accounts, and serve as a trusted consulting advisor. Prospect and qualify leads, collaborate with technical teams to create proposals and close SOW contracts, identify business improvement opportunities, build channel partnerships, maintain CRM records, and support localized marketing activities. The role is remote within specified target markets, includes up to 10% travel, and has no direct reports.

Leads parallel customer AI/ML solution design engagements from discovery through proposal. Architects enterprise solutions involving predictive analytics, NLP, computer vision, and generative AI; provides cost-benefit and TCO analysis; identifies risks and dependencies; and collaborates with Agile, system architecture, and DevSecOps teams to deploy governed, explainable, and monitored AI/ML systems.

Lead the strategy, roadmap, and execution of an AI-driven B2B SaaS platform for government contracting. Translate RFP and customer needs into prioritized product enhancements, collaborate with AI research and cross-functional teams, apply agile methodologies using Jira, and drive successful product launches and adoption.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Translates business objectives into technical requirements, user stories, acceptance criteria, process flows, data mappings, and specifications. Collaborates with stakeholders, developers, architects, and project managers throughout Agile delivery. Reviews technical artifacts, supports solution design and implementation planning, validates completed solutions, facilitates testing and demonstrations, and identifies opportunities for automation, artificial intelligence, and process optimization.

Executes systems engineering across R&D platforms, hybrid cloud, SaaS, HPC, containers, integrations, data pipelines, observability, and AI architectures. Coordinates Enterprise IT, vendors, cybersecurity, compliance, and scientific stakeholders to deliver secure, scalable, validated, and supportable solutions. Develops technical documentation, deployment plans, runbooks, monitoring, lifecycle artifacts, risk logs, and operational handoffs. Implements RAG, vector database, AI gateway, agentic workflow, and multimodal integration patterns while supporting cloud migration, change management, governance, and technical issue resolution.

Build and operate Lakehouse data pipelines, product features, machine learning and generative AI components, testing, monitoring, instrumentation, and documentation. Collaborate with product managers, engineers, and business users to clarify requirements, deliver production solutions, investigate defects, and improve reliability and performance. Participate in code reviews, technical design discussions, and backlog-driven development while following engineering standards and contributing to scalable Data & AI products.

Leads application engineering teams delivering cloud-native, AI-enabled solutions on Microsoft Azure for Medicare and Medicaid claims-processing operations. Oversees architecture, software development, quality automation, integrations, support, governance, security, vendor relationships, and large-scale implementations. Develops Azure applications as needed, mentors staff, manages cross-functional initiatives, ensures HIPAA compliance, and partners with business and executive stakeholders to improve healthcare payer outcomes.

Lead biostatistical support for Phase I–IV clinical trials, including study design, sample size calculations, exploratory and efficacy analyses, simulation studies, statistical analysis plans, regulatory submissions, and interpretation of results. Provide strategic guidance to cross-functional teams, oversee vendor-supported work, apply advanced statistical methods, and communicate technical findings clearly to technical and non-technical stakeholders.

Leads the organization’s AI program portfolio, overseeing strategic initiative planning, delivery, governance, prioritization, budgets, risks, and performance. Manages AI competitive intelligence activities, vendors, consultants, and strategic partners. Coordinates cross-functional teams and executive stakeholders, facilitates governance meetings, prepares reports and business cases, and ensures AI programs align with corporate objectives and deliver measurable value.

Develop deep learning and AI/ML models to predict cardiac events from ECG and physiological signals. Apply signal processing, analyze implantable-device datasets, build Python tools, validate algorithms, and support feasibility studies for diagnostic features. Collaborate with clinical, software, and hardware teams to improve implantable-device performance and patient outcomes.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Advance enterprise AI governance and risk management by conducting AI risk and control assessments, evaluating governance frameworks, analyzing AI architectures and data flows, monitoring risk metrics, and supporting assurance, audits, and regulatory examinations. The role partners with technology, cybersecurity, compliance, legal, audit, and business teams to promote secure, compliant, and responsible adoption of generative AI, machine learning, intelligent agents, and third-party AI solutions.

Leads the enterprise Workday platform and HR technology strategy, roadmap, governance, operational support, integrations, reporting, security, and continuous improvement. Oversees service delivery, release readiness, audit and SOX compliance, vendors, cross-functional programs, and platform-related risk. Partners with HR, Facilities, Finance, IT, Security, Legal, and Audit while leading a multidisciplinary team and evaluating AI opportunities. The role requires executive stakeholder management, strong Workday expertise, product leadership, and experience in complex regulated environments.

Lead legal counsel for AI and machine learning matters, advising engineering and product teams on intellectual property, regulatory compliance, contracts, privacy, open-source licensing, product design, and risk mitigation. Develop AI/ML legal playbooks, compliance processes, training, and IP protection strategies while collaborating with cross-functional stakeholders and presenting emerging risks to executives and the Board.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Paid summer 2027 internship developing software and AI/ML capabilities for voice-based frontline technology. Responsibilities include contributing to product functionality, analyzing requirements, designing and evaluating machine learning models, preparing datasets, developing automated tests, researching AI/ML practices, and integrating scalable, reliable models into production systems.

Design and scale automation and AI-driven solutions for security governance, risk, and compliance. Build pipelines to collect and validate evidence, integrate GRC platforms with security and IT systems, automate control monitoring and audit preparation, and deploy LLM-based workflows for testing, policy mapping, and risk reporting. Establish AI guardrails and quality controls while collaborating with security, engineering, data, and business teams to improve compliance and security posture.

Leads enterprise security strategy and operations across Microsoft MCAPS-Core. Oversees security programs, governance, risk management, threat intelligence, incident response, compliance, security reviews, performance metrics, budgets, and cross-organizational partnerships. Directs teams and suppliers, develops AI-enabled security capabilities, assesses geopolitical and operational threats, communicates executive-level risk insights, and ensures security controls are adopted without disrupting business or customers.

Develop fraud detection models and predictive features for account opening, account takeover, and identity risk. Responsibilities include investigating large datasets, identifying fraud patterns, engineering features, training and evaluating machine learning models, writing tested Python and PySpark code, supporting production deployment, monitoring model performance and drift, and presenting analytical recommendations. The role also requires adherence to data privacy, explainability, model validation, documentation, and governance standards.