Top Artificial Intelligence Jobs

As a Senior Hardware Systems Engineer, you will lead AI rack and cluster infrastructure designs, manage deployments, and collaborate with cross-functional teams to optimize data center solutions.

As a Senior Software Engineer, design and develop automation solutions leveraging AI, integrate AI-driven components, and maintain backend systems while collaborating cross-functionally to improve workflows.

Owner of company-wide applied AI initiatives: discover high-leverage workflows, buy or build LLM/agent solutions, ship and operate production integrations, ensure security-by-design, drive adoption, and maintain/sunset tools with clear operational ownership.

Lead AI architecture and strategy for enterprise AI transformation: design scalable platforms using LLMs, RAG, agentic AI, and advanced analytics; establish patterns, governance, and MLOps; guide teams from POC to production while aligning technical roadmaps with business objectives.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

As a Senior Software Engineer, you'll build AI features focused on LLMs and RAG systems, collaborating across teams to ensure robust performance.

The Senior AI Engineer will focus on building AI-driven pipelines, semantic technologies, and enterprise ontologies while mentoring and ensuring best practices in a hands-on role.

Lead product strategy for GenAI safety across Pinterest: build proactive safety frameworks, run threat modeling and red-teaming, translate policy into product requirements, define safety metrics, manage incident response, and collaborate cross-functionally to ensure equitable, scalable AI safeguards.

The role involves managing the AI product lifecycle, building LLM-based workflows, defining product requirements, monitoring performance, and collaborating with teams to ensure effective AI solutions.

Design, research, and deliver LLM and GenAI solutions for healthcare SaaS at scale: architect systems, handle healthcare data, implement PEFT and RAG pipelines, ensure responsible AI, mentor teams, and translate technical outcomes to business impact.

Lead discovery and modernization of expert-driven manual processes using LLMs, AI agents, and automation. Embed with domain experts to extract tacit knowledge, produce formal process models, design multi-step agent workflows with HITL escalation, replace legacy tools, and build evaluation and audit frameworks to ensure safe, verifiable production deployment.

Design, develop, and maintain scalable AI/ML systems and end-to-end pipelines (data preprocessing, model training, prompt engineering). Collaborate cross-functionally, optimize model performance, ensure fairness and security, monitor production systems, troubleshoot issues, mentor junior engineers, and recommend improvements to data infrastructure and tooling.

The Experience Architect focuses on building Agentforce-powered experiences, driving solution architecture, and executing strategies for generative AI technologies in a customer-facing role.

Embed with lending and finance desks to map workflows, identify AI redesign opportunities, build and iterate prototypes (RAG/agent-based), transition validated solutions to production, contribute reusable skills to a firm-wide catalog, and partner with tech, legal, compliance, and desk leadership to ensure adoption, governance, and ongoing performance monitoring.

Lead and implement AI-assisted engineering workflows within software development, focusing on scalable AI integration, CI/CD automation, and team enablement.

The Officer, Model Risk Management validates bank-wide models, ensuring compliance with regulations. Responsibilities include developing testing plans, assessing model performance, reviewing documentation, and collaborating with stakeholders for effective model governance.

Lead a team to design, build, and operate scalable, secure platform and MLOps capabilities on GCP and hybrid cloud. Drive DevSecOps, CI/CD, telemetry, and platform modernization to accelerate AI/ML and software delivery while managing delivery, budgets, vendors, and stakeholder alignment.

Lead clients in designing and deploying Microsoft Azure AI solutions. Oversee multiple projects, engage in client discussions, and support sales efforts.

Lead research and thought leadership on data management and AI readiness, produce actionable insights, advise clients and sales, present at events, mentor analysts, and improve research processes to drive client outcomes.

Lead research on AI integration in manufacturing, develop actionable insights for clients, analyze market trends, and provide expert guidance on digitalization and AI strategies.

Lead engineering of the full embodied systems stack for real-world robots, owning onboard inference, data collection, teleoperation, and on-robot RL systems. Set architecture and technical direction, deliver reliable real-time deployments, close the loop between data, models and actions, and mentor engineers to scale embodied AI in industrial environments.

The role involves building and maintaining AI infrastructure, optimizing training performance, and developing tools for robot training and data workflows.

Lead architecture and integration of hyperscale server and rack solutions, driving technical strategy with customers and suppliers. Provide technical leadership across engineering, design and business teams, resolve performance and operational issues, and steer new product development from concept through delivery with emphasis on firmware, BMC, security, and high-speed system design.

Lead the vision and execution of AI and automation strategies across multiple sectors, ensuring integration and compliance with regulatory standards while steering teams and optimizing workflows for operational efficiency.

Act as a technical partner to internal teams to design and implement LLM-powered solutions. Build prototypes and POCs, advise on system architectures, support end-to-end deployment, and keep teams current on LLM best practices and tools while prioritizing safety and responsible use.