Top Artificial Intelligence Jobs

Work with the development team to build and debug web applications: write Python backend code, create responsive HTML/CSS/JavaScript frontends, integrate frontend and backend, participate in code reviews and testing, learn frameworks and document progress.

Own day-to-day People Operations and San Francisco workplace operations, including onboarding, offboarding, benefits administration, HR systems, employee records, performance cycles, vendors, office supplies, facilities coordination, and company events. The role supports employee experience, workplace culture, compliance processes, and People programs in a fast-paced startup environment.

Design, build, and operate payment services for card processing, refunds, reconciliation, and processor integrations. Develop scalable Kotlin and Java Spring Boot APIs, optimize PostgreSQL schemas and SQL, and implement idempotency, retries, webhooks, audit trails, and reconciliation. Respond to payment incidents, conduct root cause analysis, collaborate cross-functionally, review architecture and code, and mentor engineers. The role requires strong distributed-systems, payments, PCI DSS, debugging, and production operations experience.

Design and build real-time, highly concurrent software and cloud-native systems for an AI-powered emergency communications platform. Integrate and deploy LLMs, optimize voice and audio-processing pipelines, and support speech recognition, synthesis, detection, and filtering. Contribute to infrastructure, observability, security, scalability, and fault tolerance. Provide technical leadership through architecture decisions, code reviews, mentoring, and engineering best practices.

Test and validate novel hardware systems in a hands-on laboratory environment. Responsibilities include operating test and measurement equipment, diagnosing issues, analyzing and visualizing data with Python or MATLAB, maintaining detailed records, troubleshooting experiments, and independently understanding results before proceeding. The role is on-site in the San Francisco Bay Area and requires U.S. citizenship.

Create, film, edit, and publish high-volume short-form video for GRAI’s TikTok and Instagram channels. Serve as the on-camera personality, test content formats, analyze performance metrics, and optimize for followers, saves, shares, and comments. The role requires strong charisma, independent shooting and editing skills, music enthusiasm, and a portfolio demonstrating successful short-form content.

Conduct research at the intersection of mechanistic interpretability and reinforcement learning. Develop methods to analyze language-model and agent internals, derive intrinsic rewards and training signals, run experiments, evaluate failure modes, and build reproducible research infrastructure. The fellow will own a focused project, collaborate with technical staff, communicate findings, and contribute to publications.

Leads company-wide operational excellence during rapid growth, building scalable processes and coordinating Finance, Legal, People, IT, InfoSec, Compliance, and business operations. The role resolves complex cross-functional problems, supports leadership decisions, anticipates operational risks, and builds a high-performing operations team. Candidates need high-growth and global operating experience, broad ownership across multiple back-office functions, and strong people leadership. Experience in AI, data, or developer tools is advantageous.

Conduct outbound prospecting through calls, email, and LinkedIn; research potential customers; build and maintain qualified sales pipelines; record activities in HubSpot or similar tools; meet quotas; and collaborate with Sales, Customer Success, and Marketing. The role requires onsite work in Denver three to five days weekly and occasional travel to industry conferences.

Design and deliver modern data platforms, scalable and secure ingestion and transformation pipelines, durable data models, streaming systems, and reporting layers. Partner directly with clients and business stakeholders to translate requirements into architecture, explain tradeoffs, and own delivery outcomes. The role includes production implementation across cloud data platforms, orchestration, governance, real-time technologies, and analytics or AI data consumption.