Top Artificial Intelligence Jobs

The Director of Clinical serves as Videa’s clinical representative across sales, customer success, product, and marketing. The role leads clinical discussions with dental groups, supports deal strategy, strengthens relationships with DSO clinical leaders, translates clinician feedback into product priorities, and represents the company at industry events. Candidates must have a DDS or DMD, meaningful recent chairside experience, commercial or customer-facing experience, strong executive presence, and credibility with clinicians. Travel of up to 25% is expected.

Three-month AI engineering R&D internship developing and evaluating machine learning and computer vision solutions for future sports medicine technologies. Responsibilities include creating algorithms for medical imaging and navigation, integrating software into the TESSA ecosystem, prototyping AI-enabled workflows, analyzing data, defining metrics, documenting results, and presenting findings to engineering and R&D teams.

Build and deploy applied AI features, LLM applications, VLM tools, and multi-agent systems for customer and internal use cases. Own AI systems end to end, including cloud infrastructure, integrations, access control, evaluation, model selection, monitoring, and production reliability. Collaborate with trading, operations, and backend teams to incorporate energy markets, pricing, demand, and weather data.

Leads Microsoft’s vendor AI FinOps and token economics strategy, governing demand, forecasting, allocation, consumption, optimization, and commercial accountability. Establishes approval thresholds, tracks usage and variances, links token investment to business outcomes, and embeds AI economics into vendor SOWs, renewals, and transformation initiatives. The role also drives model and usage optimization, value realization reporting, and an operating model treating AI consumption as a strategic capacity investment.

Design, implement, and operate production-quality AI framework components, benchmark and optimize large model training and inference across GPUs and Microsoft hardware, build automation and observability, drive cross-team projects from definition to deployment, and provide technical leadership and mentorship for improvements in performance, reliability, and model onboarding velocity.

Leads mixed-methods UX research for an AI productivity experience, combining telemetry, surveys, experiments, interviews, contextual inquiry, usability testing, and agentic evaluations. Identifies user needs, friction, capability gaps, retention drivers, and competitive performance; establishes experience benchmarks; influences product strategy and quality roadmaps; partners with Product, Design, Engineering, and Data Science; and mentors researchers.

Lead enterprise customer implementations from sales handoff through onboarding, training, and Customer Success handoff. Map fashion and retail workflows, teach generative AI techniques, create scalable enablement resources, monitor adoption, and provide product feedback. The role requires deep familiarity with fashion design and product-development tools, strong teaching skills, hands-on generative AI experience, organized implementation management, and willingness to travel for customer training.

Industrial Engineering Intern applies Six Sigma, DMAIC, DOE, Lean manufacturing, statistical process control, and data analysis to improve manufacturing processes. Responsibilities include conducting time studies, mapping processes, analyzing production data, designing automation prototypes, reducing waste and variation, and presenting recommendations to facility leadership.

Build and improve production AI agents for chip design and verification. Responsibilities include evaluating agent performance, diagnosing failures, developing orchestration, memory, context management, and tool interfaces, and converting experiments into scalable, reliable software. The role requires strong backend architecture, production service experience, Python or TypeScript skills, and cross-functional technical leadership.

Leads engineering tools, systems, automation, and productivity strategy. Oversees SolidWorks and PDM administration, CAD standards, system integration, document control, data governance, AI and automation adoption, Lean and Six Sigma improvement initiatives, standardization, training, vendor relationships, and change management. Partners cross-functionally to improve engineering quality, capacity, cycle time, scalability, data accuracy, and execution predictability.

Leads enterprise AI assurance and audit engagements covering Generative AI, LLMs, AI governance, Responsible AI, risk management, and AI-enabled processes. Evaluates controls for bias, fairness, transparency, privacy, hallucinations, misuse, human oversight, monitoring, and regulatory compliance. Advises senior management and the Board, identifies control gaps and emerging risks, develops audit plans, manages teams and resources, and delivers objective recommendations across the organization.

Build and deploy production software across front-end, APIs, backend services, data, and cloud infrastructure. Develop AI-enabled features and agent workflows using models, retrieval, tool calling, structured outputs, and evaluations. Apply secure engineering, infrastructure as code, observability, automated testing, and CI/CD practices. Work directly with clients, troubleshoot production systems, participate in reviews, and contribute reusable engineering patterns.

Build, test, and support AI-powered applications and workflow automations using Microsoft Power Platform, GPTs, agents, APIs, and automation tools. Configure prompts, integrations, and knowledge sources; troubleshoot workflows; conduct testing and UAT; document solutions; support deployments and users; and follow information security practices. The role offers guided development for an early-career engineer with foundational programming, AI, automation, and software skills.

Develop and maintain generative AI applications using LLMs, RAG, prompt engineering, and orchestration frameworks. Build scalable Python APIs and microservices, collaborate with engineering and product teams, and support deployment, monitoring, troubleshooting, and optimization in Azure and AWS environments. Contribute to CI/CD, automated testing, containerization, infrastructure automation, code reviews, documentation, and Agile delivery.

Leads enterprise AI governance and controls engineering, ensuring compliance with the EU AI Act, U.S. state AI laws, NIST AI RMF, and internal policy. Oversees AI risk assessments, control design, automation, monitoring, validation, audit responses, CAPA execution, OneTrust workflows, vendor partnerships, and cross-functional collaboration. Manages and develops the AI Controls Engineering team while driving policy harmonization, continuous improvement, and scalable governance technology adoption.

Engineer and operate a self-hosted AI observability platform on OpenShift. Responsibilities include deploying vendor applications, managing Kubernetes resources and stateful services, automating CI/CD, monitoring reliability, handling incidents, implementing security and governance controls, planning capacity, supporting disaster recovery, and leading vendor and cross-functional initiatives.

Engineers and operates a business-critical AI guardrails platform on on-premises OpenShift. Responsibilities include deployments, upgrades, configuration, monitoring, security, capacity planning, incident response, disaster recovery, automation, vendor management, and application onboarding. The role supports production reliability, performance, and integrations while leading technical initiatives and mentoring engineers across internal, offshore, and vendor teams.

Leads Americas partner sales strategy, go-to-market planning, and execution for Cisco’s AI infrastructure and connectivity portfolio. Manages a channel specialist sales team, drives growth across hardware and software offerings, builds executive partner relationships, oversees pipeline and forecasting rigor, and leads business transformation while fostering an inclusive, high-performing sales culture.

Leads strategy, roadmap, discovery, launch, and optimization of AI-powered digital products across web, mobile, B2C, and B2B platforms. Partners with UX, engineering, data science, analytics, marketing, and vendors to define requirements, manage releases, establish KPIs, evaluate AI models, and ensure measurable customer and business value. Presents business cases, manages stakeholder alignment, and oversees model governance, performance monitoring, and continuous improvement.

Designs and validates enterprise AI infrastructure solutions for customers, focusing on GPU-accelerated computing, Kubernetes, Linux, NVIDIA technologies, networking, storage, security, and AI platforms. Leads discovery sessions, architecture workshops, proofs of concept, demonstrations, proposals, and RFP responses throughout the sales cycle. Advises customers on agentic AI, multi-tenancy, governance, and production operations while collaborating with partners, sales teams, product management, and engineering. Develops reusable reference architectures and enables field teams.

Build AI agents, tools, data connectors, and multi-step workflows using Python, APIs, SQL, and agent frameworks. Develop retrieval strategies, evaluation sets, documentation, and weekly demonstrations for business users. Work in sandbox and read-only environments with human approval required for live-system changes. The intern must be enrolled in a relevant U.S. degree program and available for continued part-time remote work after the summer.

Build AI agents, tools, and data connectors in Python using APIs and SQL. Design multi-step LangGraph workflows with human approval, select appropriate retrieval methods, evaluate answer quality, deliver weekly demonstrations, use AI coding assistants responsibly, and document implementations. The intern will work onsite or hybrid in Cary, North Carolina and may continue part-time remotely for approximately six months after summer.

Build and operate production data pipelines and AI/ML/LLM systems across AWS and Snowflake. Responsibilities include developing real-time and batch pipelines, retrieval and embedding workflows, model serving, agent orchestration, observability, CI/CD, testing, infrastructure automation, APIs, incident response, and operational support. The role emphasizes reliable, scalable, maintainable Python systems and production deployment of retrieval-augmented and agentic AI applications.

Leads the design, development, testing, deployment, and optimization of scalable data and AI applications. Builds data models, pipelines, APIs, microservices, and agentic AI solutions for Revenue Operations. Partners with stakeholders to identify high-value automation opportunities, manages end-to-end delivery, ensures security and governance, optimizes cloud and on-premise platforms, and mentors less experienced staff while influencing strategic technology decisions.

Architects, develops, tests, deploys, and optimizes scalable Data and AI applications. Designs data models, pipelines, APIs, and microservices; applies advanced AI and statistical quality techniques; and evaluates cloud and on-premises technologies for scalability, performance, security, and cost optimization. Leads complex projects, resolves difficult technical issues, collaborates across teams, and guides others.