Top Artificial Intelligence Jobs

Leads Broadridge’s enterprise strategy, roadmap, and evolution for high-volume digital communications capabilities. Oversees channels, content, personalization, orchestration, analytics, AI, security, compliance, and scalability. Builds reusable enterprise services, prioritizes investments, develops business cases, drives adoption, and partners across technology, operations, risk, legal, sales, and client teams. Leads a multidisciplinary product organization and provides executive reporting on performance, risks, adoption, financial outcomes, and strategic progress.

Leads pharmaceutical sales planning, analytics, field-force effectiveness, territory optimization, incentive compensation, performance reporting, and dashboard development. Analyzes complex commercial data to identify opportunities, support targeting and account planning, measure marketing effectiveness, and improve sales execution. Coordinates cross-functional projects, data quality, user testing, training, and continuous improvement initiatives, including AI applications. The role also manages stakeholders and presents insights to senior leadership.

Drive sales growth for BinSentry’s agricultural SaaS and HD monitoring solutions across poultry, swine, and milling sectors. Build and manage prospect pipelines, conduct frequent on-site visits, generate qualified meetings, identify operational needs, and position supply-chain visibility and forecasting solutions. Collaborate with Customer Success, Product, Operations, and senior sales leaders on pilot deployments. Maintain CRM reporting and forecasting, and represent the company at industry events.

Drive poultry-sector sales growth across North America by building pipelines, conducting on-site customer visits, generating qualified meetings, and positioning BinSentry’s monitoring solutions. Engage poultry operators and supply-chain leaders, identify operational gaps, support pilot deployments, maintain CRM forecasting and activity records, and represent the company at industry events. The role requires deep agriculture or feed-supply-chain experience and involves 50%–75% travel.

Supports IT audit planning, SOX testing, control evaluations, risk assessments, data analytics, audit documentation, findings, reporting, and corrective-action monitoring. Uses AuditBoard and enterprise technology knowledge to identify control weaknesses, anomalies, and process improvements. Collaborates with stakeholders across the organization and contributes to audit automation, analytics capabilities, methodology enhancements, and special projects. Requires a relevant bachelor’s degree, preferably 1–2 years of audit or risk experience, and willingness to travel domestically and internationally 20–30%.

Own the full business development cycle from prospecting and qualification through discovery, solution development, negotiation, and close. Research prospects, conduct outbound outreach, lead customer meetings, coordinate samples and pilots, prepare proposals and pricing materials, support contract discussions, maintain pipeline records, and build long-term customer relationships. Collaborate with technical, research, project management, and operations teams while developing commercial judgment under mentorship.

Lead the architecture and long-term roadmap for Relativity’s machine learning platform. Design scalable training, inference, monitoring, and ML operations systems; evaluate emerging technologies; improve model performance and cost efficiency through optimization techniques; and ensure secure, reliable AI services. Partner with product, program, and data science leaders, guide cross-functional initiatives, establish engineering standards, and mentor senior engineers across multiple teams.

Leads MUSC’s enterprise learning, leadership development, organizational capability, and workforce transformation strategy. Establishes learning governance, competency frameworks, leadership pipelines, succession initiatives, executive coaching, onboarding, career pathways, and knowledge management. Oversees LMS, digital learning, simulation, AI-enabled learning, analytics, budgets, vendors, and strategic partnerships. Aligns learning across clinical, academic, research, and administrative functions while measuring impact on workforce capability, leadership, operations, patient safety, academic excellence, and organizational performance.

Lead the design, development, integration, testing, and deployment of train automation systems. Define and manage requirements, conduct trade studies, coordinate verification and validation, investigate technical issues, support field testing, and maintain technical documentation. Collaborate with product management, engineering teams, railroad customers, and other stakeholders to deliver safe, reliable, and efficient train-handling solutions. Apply systems engineering principles, advanced analytics, automation, and AI-enabled tools to improve product performance and engineering productivity.

Leads a global engineering organization of approximately 100 employees across application development, data engineering, platform engineering, AI, and technical operations. Defines engineering strategy, modernizes platforms, improves delivery, quality, security, reliability, and operational excellence, and drives responsible AI adoption. Builds leadership capacity, scalable processes, architecture discipline, and technical talent while partnering with product, data, security, operations, clients, and executives to connect technology investments with business outcomes.

Conduct quantitative social science research for government-sponsored national security projects involving AI/ML, decision support, information warfare, NLP, OSINT, language assessment, and human-machine teaming. Lead research design, statistical analysis, prototyping, experimentation, systems integration, and potential field deployment. Collaborate with multidisciplinary teams, produce scholarly outputs, and support applied research programs. The role requires advanced quantitative methods expertise, research leadership, and eligibility for a Secret security clearance.

Leads cybersecurity and systems engineering initiatives for government and critical infrastructure organizations. Responsibilities include developing security and system-of-systems architectures, requirements, Zero Trust and cyber resilience strategies, technology roadmaps, mission workflows, and analytical tools using AI and machine learning. Provides technical guidance to senior decision-makers, evaluates and transitions technologies, assesses cyber capabilities and risks, leads teams, mentors staff, and oversees multiple projects and external stakeholders.

The Chief Lending Officer provides executive leadership for all lending functions, including consumer, mortgage, indirect, home equity, business lending, collections, and lending operations. The role develops lending strategy, manages portfolio growth and profitability, oversees credit risk and regulatory compliance, leads digital lending innovation, reports to the board and regulators, and develops lending leadership talent.

Data Science Intern supporting data collection, cleaning, exploratory analysis, predictive modeling, machine learning and AI prototypes, dashboards, reporting, and technical documentation. The role collaborates with data scientists and engineers to develop business solutions and communicate findings to non-technical teams. Candidates must be enrolled in a quantitative undergraduate or graduate degree program and demonstrate analytical, communication, reasoning, and computer skills.

Build and operate production agentic AI systems, including prompts, tools, retrieval, guardrails, evaluations, observability, and rollout. Automate expert workflows, ensure compliance and safety, and establish human-in-the-loop controls. Deliver documentation, runbooks, dashboards, and evaluation harnesses while enabling teams to adopt AI safely. The role requires substantial software engineering experience, production LLM deployments, retrieval expertise, structured outputs, local model experimentation, and strong communication.

Build, test, and scale AI-powered applications using LLMs, data, integrations, and automation. Own features end-to-end, contribute to design and evaluation, implement prompt engineering, retrieval/RAG pipelines, and reusable components, collaborate across teams, and support junior engineers.

Leads enterprise technology and AI strategy, portfolio prioritization, value realization, executive decision support, and adoption initiatives. Partners with business, technology, data, risk, finance, and transformation leaders to evaluate opportunities, risks, investments, and outcomes. Develops roadmaps, business cases, strategic recommendations, value frameworks, executive presentations, and change-enablement strategies while considering responsible AI, cybersecurity, privacy, data governance, resilience, and regulatory requirements.

Lead the architecture, development, and scaling of data pipelines and infrastructure for a customer-facing Data Lake platform. Build ETL and ingestion systems using GCP, Debezium, Pub/Sub, Dataflow, BigQuery, Python, Golang, and SQL. Establish engineering standards, deliver strategic projects, mentor engineers, influence cross-functional technical decisions, optimize data services, and shape the platform roadmap.

Develop and launch Matterstack’s laboratory operations from the ground up. Responsibilities include defining lab requirements, securing external lab access, selecting and outfitting a facility, hiring and leading staff, purchasing equipment, building supplier networks, integrating laboratory IT, managing budgets, and planning future expansion and automation. The role requires close collaboration with scientists, engineers, executives, vendors, investors, lawyers, and government stakeholders, with co-location at the eventual facility and occasional travel.

Qualify inbound leads, generate outbound pipeline through AI-powered prospecting, conduct discovery calls, and close smaller deals. The role requires managing an AI-first sales workflow, using CRM and analytics, attending industry events, collaborating with Marketing and AEs, and continuously optimizing pipeline performance. It offers a path toward a full-cycle Account Executive role.

Own customer success, renewals, expansion, and churn mitigation across a SaaS account portfolio. Drive net revenue retention through consultative selling, prospecting, stakeholder mapping, adoption planning, competitive positioning, negotiation, and accurate forecasting. Partner with Customer Engineering, Product, and Support to increase usage, identify customer value opportunities, and communicate feedback that informs product and go-to-market strategy.

Manage client relationships throughout implementation and ongoing support, serving as the primary client contact. Oversee KPIs, operational metrics, strategic planning, project execution, and client goal attainment. Translate client needs into platform opportunities, represent customer feedback to Product and Engineering, and develop case studies and success materials. The role requires healthcare client success or account management experience, strong communication, project leadership, and knowledge of medication management and healthcare quality measures.

Lead the design and delivery of production AI infrastructure, including context-aware data layers, configurable autonomous agents, event-driven automations, model-agnostic inference, access controls, and workflow governance. Contribute approximately 30% of the time to full-stack product engineering and on-call support. Partner with founders on architecture, technical direction, and engineering standards.

Lead the technical direction of an AI conversational product, including agent architecture, model selection, reasoning, tool-calling, memory, evaluation, and monitoring. Build reliable browser automation and full-stack features with TypeScript and Node.js. Collaborate with early customers and engineering leadership to refine product capabilities, manage latency and costs, and deliver AI agents from concept through production.

Serve as a technical partner for enterprise customers by analyzing operational data, building AI prototypes and proofs of concept, leading technical demos, and supporting evaluations, pilots, implementations, and active deployments. Use Python, SQL, machine learning, and data science to quantify business value and communicate effectively with technical and business stakeholders.