Top Artificial Intelligence Jobs

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and adoption enablement. The role also involves collaborating on React and TypeScript user experiences, translating ambiguous requirements into scalable systems, and communicating technical tradeoffs across product, engineering, and dairy science teams.

Build and evolve Notion’s Developer Platform, including APIs, MCP tools, integration infrastructure, and internal platform capabilities. Partner with product, design, data science, and research teams to define strategy and deliver reliable, scalable features. Own production software end to end, improve platform performance and robustness, and drive high-leverage technical investments. Help developers and customers create integrations, automations, and connected workflows.

Build and deploy production AI agents for real customers. Responsibilities include orchestrating LLM pipelines, integrating APIs and tools, developing React interfaces, implementing RAG and document-processing systems, deploying serverless applications, and monitoring performance. The role requires strong Python or JavaScript skills, hands-on OpenAI and multimodal LLM experience, vector databases, agent frameworks, function calling, and full-stack integration. Applicants must be pursuing a Master's or PhD in a technical field.

Leads OCC’s AI transformation agenda by identifying business opportunities, building prioritized AI roadmaps, and owning use cases from problem definition through adoption. Translates business needs into technical requirements, evaluates AI vendors and feasibility, supports governed citizen-led development, and partners with CTO engineering teams. The role also drives AI adoption, monitors outcomes and risks, supports portfolio reporting, and advises stakeholders on AI architecture, governance, and operating-model transformation in regulated capital-markets environments.

Build and launch customer-facing AI applications by developing frontend and backend features, integrating APIs, AI services, databases, and business tools, and improving reliability and usability. Independently manage features from requirements through deployment, troubleshoot AI-generated code, and collaborate with developers. Applicants may be self-taught or early-career and should demonstrate completed applications through employment, freelance work, or personal projects.

As an AI Native Software Engineer, you'll build products using AI coding agents, translating customer needs into solutions, and ensuring quality delivery and documentation.

As an Agentic Software Engineer, you'll build and deploy production-grade AI systems, reverse-engineer existing software, guide teams, and facilitate customer discovery.

As a Forward Deployed Engineer at Remitly, you will leverage AI agents to build applications, translate customer needs, and evaluate outputs for quality and security, focusing on delivery speed and robustness.

Lead teams to build scalable AI applications, develop client relationships, manage budgets, and mentor talent in the AI field.

Lead AI strategy and client engagements for life sciences clients, drive AI and cloud data strategy, manage and mentor consulting teams, engage C-suite stakeholders, oversee risk and delivery, and build AI-capable practices across the firm.

Drive technical sales for Microsoft’s cloud and AI data platform through demos, proofs of concept, hackathons, and architecture workshops. Advise enterprise customers on secure, scalable database and analytics architectures, migrations, and deployments. Build relationships with platform leaders, resolve technical blockers, collaborate with engineering, and promote Azure data technologies including Fabric, Databricks, Purview, and Azure databases.

Designs, deploys, operates, and scales GPU/CPU infrastructure supporting Starshield national security missions. Responsibilities include managing Kubernetes and AI clusters, bare-metal and virtualized GPU platforms, operating systems, databases, monitoring, distributed storage, automation, and high-availability services. The role collaborates with AI engineers, improves service lifecycles, mentors junior engineers, and leads technical excellence. Requires Linux, Kubernetes, infrastructure automation, scripting, software development, and the ability to obtain a Top Secret clearance.

Designs, deploys, and operates high-speed data center networks supporting AI clusters and Top Secret national security missions. Responsibilities include optical and copper connectivity, network automation, capacity planning, security, performance optimization, troubleshooting, hardware and firmware integration, and collaboration with ML and engineering teams. The role also mentors junior engineers, leads technical decisions, supports deployments, and participates in on-call rotations and travel.

Designs, deploys, operates, and scales GPU/CPU infrastructure supporting Top Secret national security missions. Responsibilities include managing bare-metal and virtualized platforms, Kubernetes and AI clusters, operating systems, databases, monitoring, distributed storage, automation, and high-availability services. The engineer collaborates with AI and software teams, improves service lifecycles and performance, and supports infrastructure deployments at massive scale.

Leads GlobalFoundries’ Edge and AI Compute end-market strategy across MCU, MPU/NPU, FPGA, embedded vision, robotics, and connected-device applications. Builds market models, customer opportunity maps, competitive assessments, and technology requirements; supports design wins and customer engagements; develops market collateral and requirements documents; and partners with Sales, Product Management, Technology Development, Design Enablement, and executives to drive roadmap decisions, funnel growth, and revenue.

Lead a Model-Based Engineering capability team in defense programs, balancing people management and hands-on MBSE work. Drive MBSE adoption, develop architectural models using SysML/UML/UAF and tools (MagicDraw/Cameo/Rhapsody), guide program MBSE activities, create validation rules, staff programs, mentor engineers, and collaborate on digital engineering strategy and continuous improvement.

Lead a Model Based Engineering (MBE) section supporting US defense programs. Split time between people management and hands-on MBSE work: develop model-based architectures using SysML/UML/UAF, guide program MBSE activities, build and validate models, create model database rules, drive adoption of digital engineering practices, and mentor staff while coordinating with business and DE leaders.

Lead a Model Based Engineering (MBE) section in a hybrid role, combining people management and hands-on MBSE contributions. Staff programs, develop MBSE strategy and skills, build and validate architectural models using SysML/UML and tools, drive continuous improvement, and collaborate with business and digital engineering leads to advance DE adoption.

Own end-to-end procurement operations, including procure-to-pay, sourcing, contract lifecycle management, supplier negotiations, and procurement technology. Support accounting processes, improve supplier performance, and build spend analytics. Lead AI enablement across contract analysis, spend classification, supplier intelligence, negotiation preparation, and workflow automation. Drive build-versus-buy decisions, data integrity, stakeholder adoption, cycle-time reduction, and cost savings across life-sciences suppliers.

Lead complex customer engagements from discovery through production delivery by architecting, building, and deploying AI solutions. Write and review production code, guide senior engineers, make architecture and deployment decisions, and translate customer-specific learnings into scalable CoCounsel product capabilities. The role requires hands-on expertise in generative AI, LLM systems, enterprise integrations, distributed systems, cloud architecture, security, reliability, and production operations, along with strong technical leadership and customer communication.

Leads the design of secure cloud, hybrid, and multi-enclave architectures supporting AI workloads and enterprise systems. Develops cloud and AI technology roadmaps, integration standards, automation, Infrastructure as Code, and DevSecOps practices. Guides cloud modernization, migration, governance, responsible AI adoption, and data architecture. Provides technical leadership on emerging technologies, system reliability, security, performance, and strategic risk across cloud, on-premises, and air-gapped environments.

Manage SEO and Answer Engine Optimization across multiple B2B media brands. Monitor organic and AI search visibility, conduct keyword and competitor research, identify technical and content opportunities, provide editorial recommendations, run optimization tests, and report on traffic, rankings, citations, mentions, and visibility across Google, ChatGPT, Gemini, Perplexity, and other platforms.

Leads AI technology capability planning for Medical Affairs and Health Impact. Translates business needs into scalable solution options, evaluates build-versus-buy and enterprise platform approaches, maintains the integrated AI roadmap, identifies dependencies and risks, and coordinates with architects, engineering, security, privacy, compliance, and delivery teams. Provides independent technical guidance without requiring deep model development or data science expertise.

Develop and deploy computer vision AI/ML algorithms for remote sensing, automatic target recognition, and multimodal EO/SAR data fusion. Responsibilities include processing large datasets, researching supervised and unsupervised methods, training and optimizing deep learning models, deploying solutions to cloud environments, mentoring engineers, communicating technical concepts, and preparing documentation. The role requires an active TS/SCI clearance and expertise in Python-based machine learning frameworks.

PhD summer intern will research and build AI-agent-driven systems that automate machine learning experimentation for time-series modeling. Responsibilities include literature review, dataset preparation, baseline development, agent framework implementation, model and hyperparameter experimentation, validation, reproducibility, experiment tracking, comparative evaluation, documentation, and presenting results to the R&D team.