Top Artificial Intelligence Jobs

A 12-week marketing internship focused on identifying AI and automation opportunities, developing and testing reusable workflows and prompts, evaluating outputs and risks, creating training materials, tracking adoption and business impact, and presenting recommendations for scaling AI capabilities. The role requires strong communication, organization, analytical skills, and interest in generative AI, marketing technology, digital transformation, or process improvement.

Build and deploy AI-native healthcare products, including LLM-powered systems and agents for clinicians and patients. Own AI features from experimentation through production, while developing evaluation, monitoring, and feedback systems. Collaborate with clinicians and engineers to identify impactful healthcare applications and optimize reliability, latency, cost, and performance across models, prompts, tools, retrieval systems, and infrastructure.

Lead the AI-first vision, strategy, and roadmap for Workday’s legal business technology ecosystem. Manage the full product lifecycle for legal operations initiatives, lead product managers, advise legal and executive stakeholders, influence enterprise product roadmaps, and oversee legal technology vendors. Drive intelligent automation, predictive analytics, generative AI, integrations, cross-functional alignment, responsible AI adoption, and measurable business outcomes across contract management, compliance, e-discovery, intellectual property, and related legal operations.

Leads competitive intelligence, commercial assessments, forecasting, market research, and strategic initiatives for a clinical-stage biopharmaceutical company. Translates immunology industry developments into portfolio, indication, clinical development, and partnering recommendations. Owns forecast assumptions, applies AI and LLM tools to research workflows, guides primary and secondary research, and supports executive, investor, partner, and analyst materials.

Designs and operates controlled cyber-capability evaluations for frontier AI models and agentic systems. Builds vulnerable applications, cyber ranges, exploit-development targets, and evaluation harnesses; runs experiments; collects telemetry; analyzes model behavior and evaluation artifacts; and documents security findings. The role also develops sandboxing, containment, access, and secrets-management controls while collaborating with red-team operators, researchers, engineers, and Responsible AI stakeholders.

Supports product teams through market and competitive research, generative AI analysis, data organization, dashboards, presentations, product requirements, user stories, feature comparisons, and market positioning. Collaborates with Product, Engineering, Sales, and Marketing teams during product discussions, workshops, and sprint planning. Documents AI research practices and helps identify customer needs, trends, and business opportunities.

Own the complete enterprise sales cycle, from qualification through negotiation and close. Develop deep understanding of prospects’ revenue workflows, lead tailored demos, create value-based narratives, and build C-level relationships. Collaborate with product and solutions teams on integrations and roadmap development, refine sales messaging, and help establish go-to-market processes, tooling, and playbooks in an early-stage AI company.

Supports administration, monitoring, troubleshooting, integrations, testing, deployment validation, incident triage, usage monitoring, documentation, and user enablement for enterprise AI platforms. The role maintains connectors, authentication, indexing, permissions, platform health, resource inventories, and compliance records across multiple environments while helping improve reliability and operational efficiency.

Lead development and delivery of the AI platform execution layer (agent runtimes, model gateways, orchestration, inference, retrieval). Own roadmap, partner across teams, ensure scalable, secure, observable, multi-tenant production runtimes, and drive engineering excellence, compliance, and operational resilience for AI workloads.

Administer and maintain large-scale HPC/AI environments, including compute, storage, networking, software stacks, and Slurm scheduling. Develop automation for provisioning, configuration, and monitoring; optimize systems for benchmarking, capacity planning, and performance. Support security patching, troubleshooting, application deployment, researcher support, documentation, vendor coordination, procurement, and lifecycle management. This remote or hybrid individual contributor role requires approximately four domestic trips annually and U.S. citizenship for access to government research facilities.

Serve as enterprise strategic advisor on AI and automation by researching market and vendor trends, evaluating emerging capabilities, developing evidence-based adoption recommendations, supporting investment and roadmap planning, and briefing senior leaders. Maintain competitive intelligence, assess risks and governance implications, and collaborate with business, product, and governance stakeholders to align strategy and execution.

Develop and operationalize long-range business plans by applying strategic analysis, business judgment, and practical AI expertise. Evaluate AI opportunities, build forecasts and KPI frameworks, solve complex business problems, and translate data into recommendations for senior stakeholders. Partner cross-functionally with finance, product, marketing, sales operations, data, and technical teams while managing ambiguous workstreams and aligning contributors.

Leads end-to-end architecture and delivery of Azure, Microsoft Fabric, and Databricks data and AI solutions. Responsibilities include discovery, solution design, presales, proposals, scoping, governance, CI/CD standards, client presentations, and production deployment. The Architect guides engineering teams, reviews deliverables, manages milestones, risks, budgets, and customer satisfaction, and translates business objectives into scalable lakehouse and data-platform architectures.

Lead the reliability and scaling of Horizon AI’s autonomous platform for production GPU infrastructure. Harden agent dispatch with circuit breakers, retries, health-aware routing, and observable error handling. Build caching and event-streaming capabilities, determine and implement the orchestration strategy, introduce infrastructure as code, enable concurrent agent sessions, and own service standards, testing, CI, observability, and deployment practices. Serve as technical deputy and collaborate with Systems and SRE teams.

Build and scale Horizon AI’s platform reliability through caching, event streaming, resilient agent dispatch, observability, infrastructure as code, and safer deployment practices. The role focuses on health checks, circuit breakers, retries, backpressure, tracing, alerting, canary releases, traffic shifting, and rollback strategies. It requires measurable experience improving distributed-system reliability in Python and containerized environments, with Kubernetes or comparable orchestration expertise.

Lead development of a unified fleet knowledge model and decision-capture pipeline for autonomous data-center operations. Build data access layers across relational, telemetry, monitoring, and time-series systems; capture labeled operator decisions; negotiate data requirements with source-system owners; and own scaling considerations including vector indexing, schema evolution, retention, and analytics separation. The role requires strong infrastructure-domain modeling, PostgreSQL expertise, and experience handling time-series and event data at scale.

Leads GPU performance optimization for customer training and inference workloads. Establishes hardware-specific performance baselines, identifies bottlenecks, tunes distributed workloads, and converts optimization expertise into agent-executable improvements. Partners with customers on workload tuning, while secondarily owning internal model serving, cost- and latency-aware routing, model migration, and GPU allocation decisions.

Build and deploy AI capabilities that improve internal workflows and user experiences. The role involves discovering high-impact automation opportunities, shipping AI applications with robust evaluation practices, partnering on data and architecture, and creating reusable patterns. Over time, the engineer will develop physician- and employer-facing intelligence features. Success requires strong end-to-end software engineering, product ownership, stakeholder discovery, and experience delivering LLM-powered applications to production.

Build and productionize AI-powered features, including RAG pipelines, tool-augmented agents, and agentic workflows. Integrate AI capabilities into enterprise systems, contribute to evaluation, observability, guardrails, and outcome measurement, and maintain reusable, high-quality code. Work autonomously with stakeholders to ensure solutions fit real workflows, identify technical risks, and support handover and capability-building after delivery.

Build and operate AI platforms, LLM-powered applications, internal APIs, and shared services for Corporate IT Engineering. Develop RAG pipelines, AI proofs of concept, and infrastructure automation while deploying workloads with Docker, Kubernetes, Terraform, Ansible, and AWS. Establish AI development best practices, integrate AI across infrastructure and internal systems, and evaluate solutions for business value. Collaborate with technical and nontechnical stakeholders to improve monitoring, troubleshooting, documentation, identity, and automation workflows.

Leads LPL’s enterprise AI strategy and portfolio execution, translating strategic priorities into funded product and technology roadmaps. Owns competitive intelligence, build-versus-buy decisions, AI pricing and monetization perspectives, deployment operating models, and cross-functional alignment across business, product, technology, risk, and legal. Tracks business outcomes, resolves execution blockers, and provides leadership with reliable portfolio visibility. The role requires strong strategic judgment, operational execution, AI industry fluency, and the ability to accelerate enterprise decision-making.

Leads enterprise AI, advanced analytics, business intelligence, and data strategy. Manages multidisciplinary teams, defines analytics roadmaps, develops Power BI reporting and semantic models, integrates Microsoft Fabric and AI capabilities, and delivers predictive insights and automation. Oversees Agile project delivery, stakeholder engagement, model performance monitoring, and scalable analytics architecture across global business units.

Build and sustain BNY’s enterprise AI Risk program within the Technology Risk Management team. The role establishes governance, translates regulatory and enterprise risk principles into AI risk requirements, oversees risk identification through monitoring, closes cross-functional gaps, and prepares reporting for senior management. Success requires AI, technology, cyber, data, model, or enterprise risk expertise; regulated-industry experience; banking risk-framework knowledge; and strong influence, negotiation, and stakeholder leadership skills.

Lead strategy and roadmap for AI-native ERP product areas. Own agentic, ML, and LLM workflows; data ingestion and synchronization; reliability evaluations; and measurable outcomes such as retention, activation, and expansion. Drive ambiguous initiatives from problem definition through launch, partner with engineering and design leadership on technical trade-offs, and improve product development processes. The role requires deep experience shipping AI-powered features to production and managing complex B2B SaaS products.

Lead technical program execution for AI/ML and secure cloud initiatives across intelligence community programs. Coordinate stakeholders, prioritize backlogs, manage timelines, and oversee deliverables. Drive RMF and ATO accreditation activities, including security documentation and coordination with ISSOs and ISSMs. Support secure workspace development and assist AI/ML developers with Python, testing, documentation, architecture, and proof-of-concept solutions.