Top Artificial Intelligence Jobs

Design and develop online courses and faculty development focused on evidence-based instructional design and responsible use of generative AI. Provide consultations, create AI literacy resources, mentor faculty, align AI adoption with policy and security, and support continuous improvement of online programs and projects across campus.

Design and develop AI systems for personalized travel and concierge services, focusing on integration and optimization of various technologies.

As an AI Tools Analyst, you will research and evaluate new AI tools, create engaging content, and teach businesses how to use these technologies effectively.

Lead design and build of production Generative AI solutions (document intelligence, RAG, semantic search). Implement and optimize LLMs, embeddings, and agentic workflows, deploy on Google Cloud/Vertex AI, apply MLOps, monitor performance, and collaborate with clients to translate requirements into scalable, cost‑efficient AI systems.

Lead architecture and delivery of a neurosymbolic AI platform blending LLMs with constraint-based planning. Set AI strategy and governance, evaluate and integrate state-of-the-art models and vector infrastructure, resolve complex AI engineering roadblocks, and produce frameworks to enable large-scale, enterprise-grade autonomous planning.

The Engagement Manager at GrowthX AI will manage client relationships post-sale, ensuring their success while advocating for them within the company. Responsibilities include strategizing account growth, translating client goals into actionable plans, and maintaining a pulse on account health.

The AI Systems Engineer will design AI architectures, build training pipelines, optimize models for edge inference, and manage model lifecycles.

Lead Sutter Health's AI governance program: set operating model and policies, manage a team of analysts, conduct high-complexity reviews, draft standards, prioritize AI use cases, and coordinate cross-functional stakeholders to integrate governance across model development and monitoring lifecycles.

Lead enterprise-wide AI adoption by translating technical AI concepts for non-technical stakeholders, curating education and communications, partnering with change management and L&D, triaging requests, and building a team to drive measurable AI adoption across the organization.

Lead the development of real-time, safety-critical onboard software for embedded GPU platforms, focusing on AI perception and optimization.

Design, build, and operate core systems for autonomous agents, focusing on reliability, security, and engineering effective agentic workflows and integrations for enterprise environments.

The Principal AI Systems Engineer will architect and evolve Kindo's agent platform for automating DevOps and SecOps workflows, focusing on AI-native systems engineering, reliability, and security.

You will define and build systems for autonomous agents to operate reliably in production, focusing on architectures, integrations, and security. Responsibilities include innovative system design, experimentation, and managing production-level reliability and security for enterprise-scale applications.

Lead research to develop post-training reinforcement learning methods that convert high-throughput biological measurements into rewards for generative biological language models. Architect and run large-scale model training, work closely with experimental teams to align measurements and modeling, and iterate rapidly to publish new methods and models.

The Associate Director will lead the strategy for digital self-help and AI experiences, ensuring seamless customer support through various channels and improving operations using data-driven insights.

Early-career full‑stack software engineer shipping weekly production features and LLM-powered products (tutoring agents, copilots, dashboards). Work closely with founders and senior engineers, own small systems end-to-end, run AI coding workflows, and write evals and regression tests for AI features.

The AI Assisted Software Engineer will design and develop enterprise software solutions, utilizing AI-assisted tools, and architect scalable systems while collaborating across teams to improve business operations.

Embed with internal teams to map workflows, design and ship production agentic AI systems that automate CX, finance, ops, and developer productivity. Build LLM integrations, observability, APIs, reusable automation modules, and data products to replace manual operational work.

Embed with internal teams to identify high-leverage manual workflows and build production-grade agentic AI systems, integrations, and automation (LLM-powered interfaces, observability, APIs, dashboards) that reduce manual effort across CX, finance, ops, and logistics.

Lead delivery of enterprise-grade Data & AI governance for the S4+ programme. Embed data ownership, stewardship, quality, privacy, security, and Responsible AI controls by design. Partner with architects, track and escalate data/AI risk, and ensure governance artefacts and operating routines are BAU-ready post go-live. Provide assurance, reporting, and evidence-based decision support for programme and executive governance.

Evaluate and annotate physics content, create problem sets and technical documentation, and assess AI-generated responses for scientific accuracy. Contribute to dataset creation and provide expert feedback to improve AI model training. Collaborate remotely with project teams to ensure timely, high-quality deliverables and improve evaluation methodologies.

Lead design and delivery of enterprise Generative AI solutions (LLM, RAG, vector search, Text-to-SQL). Ensure production readiness, model lifecycle management, security, compliance, and observability. Partner with product, data, cloud, risk, and DevOps teams, and mentor AI/data engineers.

Contractor engineering SME to review and create technical engineering content for AI training: analyze documentation, develop examples and datasets, evaluate AI outputs for technical accuracy, and collaborate with teams to improve model performance and processes.

Create clear, structured rater guidelines, evaluation rubrics, and reviewer instructions for human evaluators across domains. Review and revise documentation to remove ambiguity, design scoring frameworks, handle edge cases, and collaborate with researchers and SMEs to ensure consistent, high-quality AI evaluations.

Contractor role to create original, high-quality financial documents (reports, analyses, models) with rich visuals (tables, charts, graphs) to train AI. Ensure accuracy, anonymize sensitive data, contribute long-form documents (10–100 pages), and clarify submissions as needed.