Top Artificial Intelligence Jobs

Lead day-to-day AI capability and enablement: translate AI strategy into coaching, triage, experimentation, and scalable guidance. Provide hands-on AI coaching and experimentation, manage intake and routing workflows, capture recurring patterns into reusable artifacts, coordinate cross-functional stakeholders, run enablement initiatives, and track adoption and performance metrics to improve AI usage across the firm.

Collaborate to enhance product through analytics, conduct data exploration on user behavior, and present insights to influence product decisions.

Lead system-level architecture and detailed design of ultra-low-power analog and digital AI accelerators. Translate product requirements into block-level specs, explore novel intelligence representations, develop system models with Synopsys Platform Architect, inform RTL and verification, and collaborate across compute IP, firmware, physical design, and partners while providing technical leadership and mentorship.

The Azure Cloud Engineer will design, implement, and maintain Azure solutions, govern AI platforms, and manage infrastructure automation and deployment workflows. Responsibilities include over-seeing cloud architecture, building CI/CD pipelines, and ensuring compliance with security standards while mentoring teammates in cloud infrastructure best practices.

Provide legal support for privacy and AI governance: draft and negotiate privacy/AI-related contracts, lead Privacy by Design and impact assessments, develop governance policies and playbooks, manage cookie compliance, RoPAs, and DSARs, advise product and engineering on risk mitigation, respond to customer privacy/AI inquiries, monitor global privacy and AI regulations, and engage with regulators and works councils as needed.

Design, develop, and productionize scalable AI tools for systems engineering in autonomous vehicles, integrating data for traceability, and building backend services.

The Level 3 Support Analyst - AI Specialist resolves complex technical issues for customers, focusing on AI solutions, troubleshooting, and collaborating with multiple teams. Responsibilities include investigating issues, mentoring junior staff, and providing 24/7 emergency support.

Design and deploy AI capabilities for U.S. federal agencies, focusing on integrating AI models with government systems, and ensuring ethical standards.

The role involves building deployment tools for classified environments, maintaining documentation, and developing CI/CD pipelines, requiring extensive experience in programming and cloud technologies.

As a Senior/Lead Backend Engineer, you'll design scalable backend systems and data pipelines for AI capabilities, ensuring data quality and system reliability while mentoring peers.

The role involves developing cloud-native applications and AI features for data governance using Azure and Microsoft 365, focusing on security and compliance in regulated environments.

The AI Engineer will develop deployment tools, maintain documentation, build CI/CD pipelines, and integrate systems for classified operations.

The AI Software Engineer will design and develop AI/ML models, training and fine-tuning them for engineering applications, collaborating with engineers to streamline operations and enhance software development at SpaceX.

Design and implement AI capabilities for federal agencies, focusing on scalable systems and API integrations while ensuring safety and ethics.

Lead requirements discovery and solution evaluation for AI products: run structured elicitation, produce requirements artifacts, design validation activities, evaluate vendors and pilots, manage product portfolio through evaluation/planning, ensure implementation fidelity, create SOPs and templates, and partner cross-functionally to align scope, budget, and adoption.

Evaluate and design AI-ready data center infrastructure by analyzing AI compute architectures, power and cooling needs, networking impacts, and energy strategies. Develop roadmaps, vendor assessments, and standards for high-density AI deployments across design, construction, operations, and utility coordination.

Lead architect and hands-on developer for embedded AI/ML autonomy across the full lifecycle: model design, optimization for resource-constrained flight hardware, integration with flight software and avionics, V&V and runtime assurance, cross-functional alignment, and customer-facing technical leadership for mission-critical space systems.

The Investment AI Engineer will design, build, and operate AI workflows for investment processes, collaborating with researchers and portfolio managers to improve automation and efficiency.

Lead end-to-end delivery of high-impact AI Platform and Data Systems programs: define scope, plan milestones, track dependencies, manage risks, align executives, use AI to automate program workflows, and drive continuous improvement across cross-functional engineering teams.

The AI & Automation Solution Architect will lead the deployment of AI and automation solutions, enhance business unit efficiency, and develop strategic initiatives for technology adoption.

Design and build scalable data pipelines and backend systems for AI solutions. Lead architecture, code quality, security guardrails, and review AI-accelerated output. Partner with product, data science, and client teams to transform pipeline outputs into actionable insights. Own full feature lifecycle from prototyping with agentic tools to production-grade deployments.

The AI Engineer will research and develop AI/ML models, particularly LLMs, and collaborate in R&D to create innovative communication solutions.

The AI Strategy & Operations Advisor will operationalize AI strategies, build governance frameworks, assess AI maturity, and advise leadership teams.

Lead change management initiatives for AI transformation, focusing on stakeholder engagement, training, communication, and organizational readiness.

Lead design and delivery of high-throughput, low-latency APIs and backend services. Define API standards (REST, GraphQL, OpenAPI), drive performance, observability, scalability, and resiliency. Coach engineers, participate in design reviews and incident response, and build frameworks to simplify API development for product teams.