Top Artificial Intelligence Jobs

Support integration of LLMs and developer tools into engineering workflows, run internal AI enablement sessions, identify workflow bottlenecks and implement AI-driven automations, liaise between product managers and developers, and assist with sprint management and project tracking.

Define and validate AI-powered product capabilities by identifying emerging technologies, building prototypes, evaluating platforms, and designing scalable architectures (LLMs, RAG, semantic search, vector DBs). Architect AI agents and secure integrations across cloud platforms, develop reference architectures and reusable capabilities, and partner with product and engineering teams to deliver production-ready, governed AI solutions.

Design, build, and deploy AI/ML and Generative AI capabilities within enterprise integration and middleware platforms. Develop scalable APIs, microservices, and data pipelines; implement intelligent monitoring, anomaly detection, and predictive workflows. Collaborate with cross-functional teams, follow SDLC and deployment practices, provide production support, and participate in on-call rotations while documenting designs and troubleshooting issues.

Lead design and implementation of production agentic AI systems that integrate LLMs with Teak's platform APIs. Build RAG pipelines, MCP/tool integrations, evaluation frameworks, and AI infrastructure on cloud (AWS/GCP/Azure). Contribute backend services in Python, ensure compliance and safety, and operate systems in production including observability and on-call duties.

Design and implement an end-to-end generative AI video pipeline that converts lesson specs into inspected, versioned videos. Build agentic orchestration, programmatic animation, TTS narration, evaluation/judging systems, symbolic verification of worked examples, and scalable Python/TypeScript media infrastructure.

Lead and contribute to applied AI/ML projects across the SDLC: mentor teams, design and implement LLM-based solutions (RAG, copilots), architect integrations, manage data persistence choices, CI/CD, and present solutions to clients while influencing technical direction.

Lead and deliver Applied AI implementations across full SDLC: setup, requirements, design, development, testing and deployment. Manage risks, run stakeholder interviews and workshops, build client relationships, support PMO and change management, and contribute to mentorship, recruitment and internal practice growth.

Lead delivery of Applied AI projects across the SDLC: setup, requirements, design, development, testing and deployment. Facilitate executive workshops, manage client stakeholder alignment, identify and mitigate risks, drive organizational change management for adoption, mentor team members, contribute to recruitment and methodology improvements, and manage project planning, budgets, resources, and status reporting.

Lead delivery of applied AI implementations across full project lifecycle; run workshops and stakeholder engagements; manage risks, client relationships, PMOs and teams; drive change management and adoption; mentor staff and support business development and internal growth initiatives.

Work on applied AI consulting engagements: create client-ready deliverables, document requirements, test systems, lead junior staff, support PMO reporting, run stakeholder workshops, train users, and build client relationships.

Lead and mentor engineering teams in applied AI/ML projects. Define architectures, propose technologies, build reference implementations, contribute code and CI/CD pipelines, and work with clients on enterprise deployments (LLMs, MLOps, RAG, vector/graph databases). Shape engineering practices and influence business development.

Conduct independent, governance-focused research on AI scheming and loss-of-control risks; produce reports, papers, legal analyses; collaborate with technical teams, policymakers, and external researchers; mentor junior researchers and shape team strategy.

Participate in a 14-day remote pilot using a qualifying Google Pixel as primary device. Interact naturally with apps that generate notifications, complete daily notification-sorting tasks, submit short daily feedback/check-ins, and report technical issues. Maintain confidentiality and follow provided instructions.

Participate in a 14-day notification-sorting pilot using a qualifying Google Pixel device. Interact naturally with apps that generate notifications, complete daily sorting tasks and short feedback/check-in forms, report issues, and follow confidentiality and project instructions.

Remote, 14-day participant study using a Google Pixel 9+ as your primary phone. Interact naturally with apps that generate notifications, complete daily notification-sorting tasks (10–15 minutes/day), submit short feedback forms, report issues, and follow confidentiality and project instructions.

Participate in a 14-day notification-sorting pilot using a qualifying Google Pixel device. Interact naturally with apps that generate notifications, complete daily sorting tasks, submit short daily feedback/check-ins, and report technical issues while following confidentiality and project instructions.

Participate in a 14-day remote pilot using a qualifying Google Pixel device to naturally interact with notification-generating apps, complete daily notification-sorting tasks, and submit short daily feedback and documentation. Must follow instructions, report technical issues, and maintain confidentiality.

Participate in a 14-day remote notification-sorting pilot using a qualifying Google Pixel device. Interact naturally with apps that generate notifications, complete daily sorting tasks and short feedback forms, report issues, and follow confidentiality and project instructions.

Participate in a 14-day remote pilot using a qualifying Google Pixel device to interact naturally with apps that generate notifications. Perform daily notification sorting/organization tasks (10–15 minutes/day), submit short daily feedback/check-ins, follow project instructions, report technical issues, and maintain confidentiality per the User Agreement. Completion yields a one-time $150 payment.

Participate in a 14-day remote pilot using a qualifying Google Pixel device to interact naturally with apps that generate notifications, complete daily notification-sorting tasks, and submit brief feedback/check-ins. Must follow instructions, report issues, and maintain confidentiality.

Build end-to-end product features for an AI-native assistant: design polished UI/UX, implement backend plumbing, and ship LLM-powered experiences across web, desktop, and mobile. Work on suggestion engines, workflow builders, team-shared automation surfaces, and trusted agent interactions. Be a foundational full‑stack hire influencing product direction and delivering a fast, delightful experience.

Remote participants who are native or expert-level Italian speakers will use a qualifying Google Pixel device for 14 consecutive days to interact with notification-generating apps, complete daily notification-sorting tasks, and submit short daily feedback. Participants must follow confidentiality terms and complete background screening. Project pays $250 upon successful completion.

8-week in-person/virtual fellowship teaching entrepreneurship with an AI focus. Weekly 60-minute sessions in San Francisco, virtual mentorship, optional networking events, and mandatory Pitch Day. Participants collaborate in cohorts, learn product-building, receive mentorship from industry experts and investors, and may pitch for funding. Designed for students (high school to PhD) with technical aptitude, communication skills, leadership, and active participation.

Lead design and delivery of the AI payment platform: build and productionize ML/LLM models, operate MLOps and inference services, ensure low-latency payment integrations, own AWS and hybrid deployments, enforce PCI-DSS, implement observability, and mentor the engineering team.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.