Top Artificial Intelligence Jobs

Graduate intern will research uncertainty quantification and hallucination detection for multi-turn LLM-based scientific assistants, develop probing methods for internal representations, build evaluation pipelines on HPC/multi-GPU systems, run experiments across scientific domains, and produce research code, documentation, and publications.

Conduct ML/AI research for power system operations, develop LLM/NLP-based solutions, collaborate with stakeholders, contribute to research papers, and work independently on projects.

Lead presales for data and AI initiatives: design and present Microsoft Fabric-based data platforms, run discovery and AI workshops, scope proposals, translate technical designs into business value, mentor delivery, and support go-to-market and thought leadership.

The Senior Product Manager will lead the roadmap for AI-based shopping experiences, collaborating with engineering, design, and data teams to create user-friendly AI interactions and improve product engagement metrics.

As an AI Product Engineer, you'll develop AI-driven software solutions, collaborate with teams, and ensure high quality and performance of products.

Design and implement geospatial components of an agentic AI system: build ArcGIS-based import/validation/storage of authoritative hazard geometry, develop deterministic polygon validation and REST APIs, integrate LLM-driven geo-language features, implement secure gov-cloud hosting and audit logging, and coordinate with alerting SMEs and system agents to ensure standards-compliant, human-approved alert areas.

Support AI systems, automation, and data analysis initiatives to improve operational efficiency. Perform basic AI-related analyses and assist with automation platform tasks under supervision.

Own product vision and execution for Agent Opus, an AI-native multimodal video creation product. Translate AI research into delightful user experiences, prototype rapidly with engineers and designers, conduct user research, prioritize ruthlessly, and ensure high aesthetic quality for short-form video creation while driving product-market fit.

Build and deploy AI-native internal tools, custom agents, shared context systems, and integrations to improve marketing, creative, and operations. Own GEO/AEO visibility, reduce hallucinations via governed context, automate data flows and personalized creative, and drive adoption from prototype to production while collaborating with marketing leadership.

Design, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos) for AI/ML workloads. Own control plane and etcd lifecycle, build operators and controllers in Go, enable GPU training/inference, implement IaC and automation, improve observability and platform reliability, participate in on-call incident response, and mentor engineers.

Build and evaluate LLM-driven applications and agentic systems for clinical workflows. Design and run systematic evaluation pipelines with clinician-built gold datasets, version prompts/pipelines, measure effects before shipping, and integrate AI features into clinical products while championing evaluation-driven development and regulatory-aligned documentation.

Coordinate and execute residential and hospitality marketing projects including branding, brochures, digital and direct-mail campaigns, website and application maintenance, copywriting, vendor coordination, CRM communications, media planning/buying, event support, and creative development of sales materials.

Develop Python adapters, API integrations, and AI agent workflows using OpenAI/Claude and LLM tooling. Build AI-powered documentation, calling/communication agents, test and optimize solutions, and collaborate with senior engineers on healthcare AI projects.

Design, build, and deploy autonomous AI agents and multi-step workflows for revenue cycle management, scheduling, claims processing, and clinical documentation. Implement tool-using agents with function calling, retrieval systems, memory, planning, and optimization. Collaborate with product and clinical teams, ensure HIPAA-compliant secure deployments, and continuously monitor and improve model accuracy, latency, and cost.

Design, build, and maintain AI-driven full-stack solutions and microservices; implement CI/CD and automation; develop model deployment pipelines; leverage data, cloud, and LLM/RAG/vector DB technologies; provide technical leadership and support for commercial accounts.

Design, build, and productionize ML, NLP, and generative AI (LLM/RAG) solutions for Navy maintenance and logistics data. Implement end-to-end ML pipelines, MLOps in AWS GovCloud, vector search, model governance, and documentation for Government accreditation; collaborate with data scientists, engineers, and Navy SMEs.

Build developer tooling, demo apps, and integrations for secure AI agent experiences at Auth0. Design infrastructure, adapters for agent runtimes, and ensure resilient, secure agent-to-service communication. Contribute to open source, write developer-facing docs, mentor engineers, and lead architecture and design for production-grade systems.

Build developer tooling, demo applications, and integrations enabling secure AI agent experiences. Contribute to open source, docs, and architect scalable, resilient systems that integrate LLM APIs, agent runtimes, and identity/auth standards (OAuth2/OIDC/JWT). Mentor engineers and collaborate with product, design, and security teams.

Lead sourcing, diligence, and financial modeling for AI-native seed investments. Build investment theses, pressure-test technical claims, model returns across scenarios, and defend recommendations to partners. Source founders, represent the firm in AI communities, support portfolio technical decisions, and co-run the firm's Residency while contributing to internal AI tooling and workflows.

Design, build, and deploy LLM-powered agents and retrieval systems that autonomously execute multi-step healthcare tasks; create evaluation and monitoring infrastructure to measure and improve model quality; ship copilots and assistive features; iterate from production signals and make scalable architectural decisions.

Build and operate end-to-end AI-native production systems connecting agents, data pipelines, workflows, payments, and operations. Lead backend, data infrastructure, cloud deployment, CI/CD, monitoring, incident handling, and AI application development (LLMs, RAG, agents). Translate ambiguous business goals into technical plans and collaborate across engineering, product, and cross-border teams.

Lead architecture and engineering across multiple teams and principal engineers to accelerate delivery, optimize cloud costs, create reusable foundational code and POCs, mentor engineers, resolve high-priority incidents, align technology strategy with vendors, and drive portfolio-level innovation and process improvements.

Lead technical discovery, design, prototype, and implement customer-facing GenAI solutions. Create solution architecture, security, evaluation, and operational plans; integrate with customer systems; drive production launches and reusable patterns.

Design and operate GPU/accelerator infrastructure and inference platforms for large ML models. Build Kubernetes-based scheduling, multi-tenant GPU sharing, autoscaling, model serving, IaC (Terraform/Helm) for EKS, CI/CD for model artifacts, observability for latency and throughput, and cost/capacity optimization across heterogeneous accelerators.

Lead testing strategy for AI-powered legal products: build evaluation frameworks, design test plans, measure AI outputs, run regression and UAT, identify risks, and partner with engineering to improve quality and scale the testing function.