Top Artificial Intelligence Jobs

The role involves enhancing kernel performance and AI-assisted tooling through developer workflows, observability infrastructure, and optimization techniques, collaborating across engineering teams.

Responsible for delivering technical enablement and adoption services for OpenAI's product suite, training customers on usage, and ensuring sustained product adoption and measurable business value.

As a Full Stack Engineer, you'll develop platform capabilities, collaborate with teams to implement solutions, and integrate new technologies while ensuring architecture and security are sound.

The Director of AI will lead AI and machine learning initiatives, develop AI architecture for humanoid robots, oversee production-ready AI systems, and manage team growth and budget.

Partner with research teams to define hiring priorities and execute strategic searches for top AI/ML researchers and research engineers. Proactively source, evaluate, and close highly specialized technical candidates, advising on leveling and compensation while collaborating with sourcing partners through complex searches.

Provide technical support and troubleshooting for complex customer issues, use scripting and OpenAI tools to automate processes, partner with engineering and product teams to resolve bugs, improve support systems, and scale operations while participating in on-call and weekend coverage.

As a Technical Mentor at Udacity, you will provide personalized support through video calls, conduct technical deep-dive sessions, and host group Q&A sessions to assist learners in AI and related fields.

Design and develop AI-powered applications, implement agentic workflows using modern orchestration frameworks, and manage multimodal datasets while staying current with AI trends.

Support development and deployment of generative image models on Azure, including training/fine-tuning, prompt engineering, image preprocessing/post-processing, integration into tools, testing, documentation, and establishing repeatable workflows for industrial imagery.

The AI Automation Manager will lead AI transformation efforts across the organization, driving automation processes and ensuring successful project execution while collaborating with various teams to enhance efficiency and ROI.

Lead EE/SW integration and build end-to-end toolchains for vehicle software: CI/CD, ECU flashing, HIL/SIL test orchestration, telemetry ingestion, ML-driven test prioritization, anomaly detection, NLP log triage, and dashboards to gate releases and enable scalable vehicle validation workflows.

Responsible for the full lifecycle of AI/machine learning on resource-constrained hardware, including data ingestion, model development, and deployment on embedded devices, while working closely with cross-functional teams.

Design and implement workflow automations and AI-enabled solutions using Microsoft Power Platform, Copilot, and related tools. Integrate Microsoft 365, Databricks, and enterprise systems, build connectors/APIs, coach users on Copilot adoption, ensure governance and responsible AI, and monitor automation performance and impact.

The Engineer III - AI designs intelligent systems, leads AI reliability efforts, collaborates on performance metrics, mentors engineers, and focuses on effective communication to solve complex issues.

As a Senior Formal Verification Engineer, you will develop verification strategies and environments for AI SoCs, ensuring the correctness of digital designs through formal verification methods and collaboration with engineering teams.

Build and maintain LLM/AI-powered applications (conversational interfaces, automations, computer vision), integrate AI with internal systems and Azure, implement testing and governance for AI behavior, monitor deployments for performance and cost, respond to incidents and drive improvements.

Lead and hands-on manage Seekr's AI infrastructure: design, build, and scale Kubernetes-based systems across cloud, on-prem, and edge; mentor engineers; automate operations; handle incidents and security; collaborate with software teams to ensure reliability, performance, and observability.

The Sr. Software Engineer will lead AI product development, integrate AI solutions across workflows, and mentor team members while ensuring system scalability and resilience.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.

Lead the enterprise cloud and application transformation strategy, focusing on App to Cloud modernization, driving microservices adoption, guiding development practices, and providing leadership across teams.

Lead product vision and execution for personalization systems and a new customer-facing LLM-powered AI experience. Own roadmap, partner with ML/engineering/design, launch personalization and conversational AI features, run experiments and evaluations, and drive retention, conversion, and long-term revenue.

Lead design and delivery of a scalable data and AI platform for supply chain operations. Architect and build Databricks/AWS-based ETL/ELT, streaming, CDC, RAG and agentic AI solutions; implement data models, quality frameworks, observability, and production reliability; manage and mentor a high-performing engineering team while partnering with product and business stakeholders to deliver production-ready systems.

Lead and expand SEO, GEO, AEO, and AI Search capabilities; define services, delivery frameworks, and AI-enabled operations; drive client strategy, growth, retention, and profitability; establish AI governance, workflows, KPIs, and training; mentor teams and represent the agency as a thought leader.

The AI Platform Engineer will own distributed training and inference operations using Ray on GPUs, manage the lifecycle of machine learning models, optimize performance, and integrate reinforcement learning. Key responsibilities include overseeing the Ray ecosystem, configuring training and inference systems, and ensuring quality and performance metrics are met throughout the model lifecycle.

Design and deliver production AI agent solutions for Veeva Quality Cloud to extract structured data from documents. Lead data flow, model selection and integration, prompt design, orchestration, and collaborate with engineering and UX to automate quality processes. Evaluate AI agents and present capabilities to stakeholders.