Top Artificial Intelligence Jobs

Designs, builds, debugs, and optimizes scalable AI systems, including inference infrastructure, APIs, distributed services, and platform components. The role delivers secure, reliable, high-performance AI experiences across audio, image, video, and safety applications. Responsibilities include full-stack development, cross-team collaboration, architectural innovation, rapid issue resolution, and end-to-end feature delivery for systems serving millions of customers.

Leads Cloudflare’s growth sales segment targeting AI and startup accounts. Owns revenue acquisition and expansion, recruits and develops a high-performing sales team, positions Cloudflare’s infrastructure and developer products, converts self-serve usage into enterprise agreements, and builds executive relationships with startups, founders, and venture capital partners.

Lead design and build of New York Life's enterprise AI platform and agentic solutions. Work across cloud-native infrastructure, model and agent lifecycle, RAG knowledge systems, multi-agent orchestration, MLOps/LLMOps, governance, CI/CD, observability, and production operations. Partner with security, legal, and architecture to embed responsible AI and deliver production-ready, compliant AI systems.

Develop and deploy AI/ML models for consumer banking use cases including lead scoring, next-best-action, retention, cross-sell, and customer prioritization. Define success metrics, analyze complex datasets, communicate recommendations to nontechnical stakeholders, and partner with product, technology, governance, risk, and operations teams. Ensure models meet regulatory and model risk standards through documentation, monitoring, drift detection, and retraining.

Build AI-powered tools that accelerate nuclear engineering workflows, including safety and physics analyses, neutronics simulations, and automated ticket, design-tree, and file-management processes. The role embeds with engineering teams to identify bottlenecks, prototype solutions, and deploy production-grade Python tools using LLMs, AI agents, RAG, and tool-use architectures. Work is fully onsite, may require extended hours, and requires US citizenship with the ability to obtain and maintain a Q clearance.

Develop and deploy advanced AI and machine learning models for branch network optimization, resource allocation, sales effectiveness, and workforce performance. Work with geospatial, demographic, transactional, and behavioral data; collaborate with business, governance, UX, and technology stakeholders; translate model results into business recommendations; and support regulatory model reviews. Apply techniques including spatial and graph-based modeling, reinforcement learning, optimization, and behavioral modeling.

Operate and improve a high-throughput AI security platform running on AWS EKS. Responsibilities include Kubernetes operations, troubleshooting, upgrades, Terraform and Helm infrastructure management, autoscaling, performance testing, observability, SLOs, incident response, and implementation of cloud security controls such as IAM, secrets management, encryption, network controls, image hardening, patching, ATO evidence, and continuous monitoring.

Leads multiple engineering teams building internal platforms, web applications, AI-powered workflows, integrations, data pipelines, and automation. Sets technical direction, oversees architecture and delivery, partners with product and business stakeholders, and develops engineering talent. The role emphasizes front-end development with TypeScript and React, CMS integrations, internationalization and localization, scalable SaaS systems, AI-enabled marketing workflows, and operational productivity improvements.

Leads the AI and platform engineering team responsible for Procore’s enterprise Claude rollout, MCP roadmap, AI workflow infrastructure, and Atlassian and Slack administration. Owns platform roadmaps, adoption, change management, governance participation, go-live readiness, vendor and contractor engagements, and team development. Partners with Security, GRC, Legal, Identity, Enterprise AI, and business stakeholders to operate enterprise AI and collaboration platforms securely and effectively.

As a Backend/AI Engineer, you'll integrate LLMs, optimize document processing pipelines, and improve API design for document understanding tasks.

Designs secure, scalable cloud-enabled architectures for AI-assisted modernization of legacy Marine Corps systems. Advises customers and technical teams, recommends AI/ML, data, cloud, and DevSecOps patterns, manages technical risks, and builds hybrid deterministic-agentic pipelines using LangChain and LlamaIndex. The role also fine-tunes large language models, integrates code-as-data techniques such as ASTs and control/data flow graphs, and develops production AI systems using Java, Python, APIs, and Databricks.

Leads the Business Insurance Data Engineering portfolio and enterprise AI-ready data strategy. Responsibilities include building governed data products, semantic layers, data intelligence capabilities, pipelines, and foundations for analytics, machine learning, generative AI, RAG, and agentic AI. The role oversees modernization, governance, reliability, risk, budgets, vendors, workforce development, portfolio delivery, and executive partnerships. It requires extensive data engineering leadership, strong insurance expertise, and experience managing large technical organizations and complex enterprise data ecosystems.

Design, deploy, and troubleshoot AI automation solutions across interconnected systems. Develop reusable prompts, workflows, copilots, intelligent process automations, and RAG pipelines. Integrate APIs, databases, and enterprise platforms while improving automation coverage, efficiency, and AI performance. Collaborate with stakeholders to define use cases and metrics, optimize token usage and LLM costs, and participate in responsible AI governance and risk reviews.

Lead company-wide AI transformation by designing unified distributed AI platforms, integrating frontier LLM ecosystems, and embedding with autonomous driving engineering teams. Build scalable LLMOps and MLOps pipelines, optimize GPU infrastructure and inference performance, develop AI-native engineering tools, and establish technical roadmaps. The role combines hands-on systems engineering, enterprise AI platform development, cross-functional influence, and strategic leadership across perception, prediction, planning, control, and simulation teams.

Leads post-deployment application support for enterprise SaaS and AI/ML platforms. Owns production reliability, uptime, SLAs, incident response, root-cause analysis, monitoring, automation, documentation, compliance, and customer communications. Coordinates global support teams, establishes scalable support processes, mentors engineers, and serves as the senior technical escalation point for healthcare customers operating regulated, mission-critical systems.

Lead NVIDIA’s AI for Quantum product strategy, roadmap, and go-to-market initiatives. Translate AI and quantum research into scalable platforms, developer tools, APIs, libraries, and workflows. Partner with researchers, engineers, quantum hardware companies, institutions, and developers to identify applications across quantum design, control, calibration, error correction, and algorithms. Evangelize the platform through technical communications, identify new AI, HPC, and quantum opportunities, and present product strategy to executives.

Serve as an HR Generalist for Microsoft AI, advising leaders on hiring, onboarding, talent development, and organizational effectiveness. Partner with Recruiting and HRBPs, lead people initiatives, use workforce data to inform decisions, and improve employee experience in a fast-growing, agile environment.

Conduct research on efficient and agentic AI systems alongside Microsoft researchers. Collaborate on machine learning and systems research, evaluate model quality, speed, cost, and performance, develop or assess AI applications and agents, analyze experiments, present findings, and contribute to research publications or open-source projects during a 12-week internship.

Leads customer and partner architecture engagements for Azure and Databricks data, analytics, AI, and modernization solutions. Designs enterprise-scale architectures, conducts technical assessments and proofs of concept, resolves deployment and scalability blockers, and guides production implementations. Drives Azure consumption, Databricks adoption, operational excellence, security, governance, and business outcomes while collaborating with sales, engineering, product, and partner teams. Creates reusable architectures and technical guidance, influences technology roadmaps, and provides technical thought leadership.

Lead technical pre-sales engagements for Microsoft cloud database and analytics products. Deliver PoCs, demos, hackathons, and architecture workshops; design secure, scalable solutions; resolve technical blockers; collaborate with engineering; and evangelize Fabric, Databricks, Purview and Azure database technologies to accelerate customer migrations and adoption.

Designs and implements compiler, runtime, and system software for Microsoft AI accelerator devices. Responsibilities include transforming code through LLVM IR, developing JIT compilation and dynamic code-generation features, deploying application code, and collaborating with hardware architects and ML developers. The role requires hands-on C/C++ and Python development, optimized systems-level programming, Unix-like platforms, and compiler framework experience.

Provide principal-level technical leadership for Microsoft's Perception AI platform. Define architectures for generative AI, RAG, memory, reasoning, agentic systems, identity, authorization, safety, reliability, and distributed cloud services. Establish reusable platform patterns, security guardrails, evaluation and observability frameworks, and cross-organization technical strategies. Partner with engineering, research, product, security, privacy, and compliance teams while mentoring senior technical leaders and driving scalable, secure, compliant enterprise AI solutions.

Develop AI models and algorithms for biomolecular modeling and simulation. Responsibilities include integrating large-scale molecular and laboratory data, writing research code, orchestrating data generation and training campaigns, developing deep learning architectures and generative models, publishing research, and collaborating with an interdisciplinary international team.

Develop and operate production AI framework components, runtimes, benchmarking systems, performance tools, and integrations. Benchmark and optimize large language model training and inference across GPUs and Microsoft hardware, build automation and observability, investigate cross-stack performance and reliability issues, and deliver scalable platform improvements. Principal engineers additionally set technical direction, lead multi-team initiatives, define architectures, and mentor technical leaders.

Design, build, and operate production agentic AI services using LLM orchestration, RAG, tool integration, evaluation frameworks, and secure AI patterns. Write production C# and Python, integrate Azure services, and own solutions through deployment, monitoring, incident response, and continuous improvement. Lead technical execution, mentor engineers, and collaborate with product, research, platform, and support teams to improve customer support automation, reliability, and resolution outcomes.