Top Artificial Intelligence Jobs

Design and architect large-scale GPU clusters for AI training, inference, and HPC workloads. Collaborate with customers, OEMs, product teams, and engineering on compute, networking, storage, topologies, performance modeling, validation, debugging, and new product deployments. Support enterprise cluster bring-ups and provide technical guidance on NVIDIA GPUs, NVLink, networking architectures, and distributed training technologies.

Serve as a strategic HR advisor to Microsoft AI leaders, guiding organizational growth, change, workforce planning, talent strategy, leadership effectiveness, and employee experience. Assess organizational health, design workforce structures and operating models, identify talent risks, and collaborate with Recruiting, HR Business Partners, and Centers of Excellence. Lead a team, coach senior leaders, and deliver people solutions supporting business agility and scale.

Lead architecture and hands-on development of Microsoft's AI Safety Platform, building observability, detection, investigation, threat-hunting, and distributed data-processing capabilities. Design attack detection infrastructure, correlate high-volume telemetry, apply AI security research, conduct security and privacy reviews, and partner across engineering, science, and incident response teams. Mentor engineers and provide technical leadership for complex AI security systems.

Leads PCN governance, headcount management, workforce planning, organizational design, and hiring alignment for Microsoft AI Experiences. Builds workforce and budget insights, ensures governance and compliance, and identifies organization-wide operational improvements. Partners with Finance, HR, Recruiting, Legal, Engineering, and executives to lead cross-functional projects, influence without authority, improve processes, and drive adoption of scalable solutions.

Design secure, scalable AI and cloud architectures for strategic Microsoft partners. Lead architecture sessions, develop proofs of concept and pilots, provide technical implementation guidance, modernize legacy systems, and help partners build AI practices, reusable intellectual property, agentic solutions, and managed services. Collaborate with partner executives, architects, developers, and Microsoft engineering while integrating AI models with enterprise data, applications, APIs, security, governance, and observability.

Lead research, design, and evaluation of large-scale detection models for automated, fraudulent, and malicious activity. Define measurement frameworks for noisy or incomplete ground truth, mentor data science practitioners, advance AI/ML techniques, and shape long-term detection strategy. Partner with ML Engineering, Data Engineering, and Product teams to influence roadmaps and protect Cloudflare customers from evolving threats.

Lead Uber’s security strategy and engineering for AI systems, including LLMs, agents, and AI-driven infrastructure. Design and ship secure, scalable systems; establish architecture for identity, access, monitoring, threat detection, data protection, and zero trust; investigate incidents; influence company-wide engineering practices; mentor senior engineers; and partner with security, infrastructure, product, and AI/ML leaders. The role requires 10+ years of software engineering experience and deep cybersecurity expertise.

Win and grow enterprise business for Braze AI by engaging executive customers, leading AI solution discussions, quantifying business impact, supporting proofs of value, enabling sales colleagues, and communicating customer needs to product and marketing teams. The role requires strong enterprise sales or consulting experience, executive presence, business acumen, technical enthusiasm, relationship-building, and execution skills.

Drive enterprise sales and growth for Braze AI by engaging senior customer stakeholders, leading AI solution discussions, quantifying business impact, supporting proofs of value, enabling sales colleagues, and communicating customer needs to product and marketing teams. The role requires strong executive presence, business acumen, technical curiosity, relationship-building, structured execution, and at least five years of relevant experience in enterprise sales, consulting, marketing technology, AI, or marketing.

Lead enterprise sales for Braze AI Decision Studio by developing new and existing senior customer relationships, guiding AI solution evaluations, quantifying business impact, and supporting proofs of value. Partner with sales, pre- and post-sales, implementation, product, and marketing teams. Train colleagues on reinforcement learning in marketing, communicate customer needs, and represent Braze AI at executive meetings, events, conferences, and trade shows.

Supports SharkNinja’s AI transformation by analyzing business data, developing AI prompts, cleaning datasets, building dashboards and simple models, identifying trends, and prototyping agentic workflows. The intern collaborates with technical and business teams to deliver actionable insights, improve analytical processes, identify data quality issues, and communicate findings to stakeholders. The role is designed for current or recently graduated students with experience in AI tools, analytics, software engineering, or related disciplines.

Lead performance engineering for AV and robotics workloads: engage customers, develop parallel algorithms and GPU-optimized libraries, profile and tune GPU kernels, optimize distributed transformer communication using NCCL/NVSHMEM and interconnect strategies, and collaborate with architecture and system teams to influence next-generation hardware and software.

Develop and deploy scalable AI, machine learning, deep learning, and data analytics solutions on cloud GPU platforms. Build customer proofs of concept using NVIDIA hardware and software, collaborate with cloud providers and sales teams, present technical solutions, support customer roadmaps, and resolve technical issues. Requires solutions or sales engineering experience, cloud deployment expertise, ML frameworks, GPUs, CUDA, DevOps/MLOps technologies, scripting, and strong communication skills.

Develop and optimize GPU-accelerated numerical linear algebra libraries for AI and high-performance computing. Design kernel generation, JIT compilation, matrix operations, and operator fusions; lead projects involving multiple engineers and teams; provide technical mentorship; collaborate with product management and customers on requirements and roadmaps; and improve performance and maintainability through architectural enhancements.

Lead a team to optimize algorithms for Deep Learning and Machine Learning, define technical initiatives, and collaborate with various teams for hardware and software innovation.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Design and develop a C++-based system for simplifying and accelerating computing in DL and HPC, alongside evaluating performance and improving library quality.

Lead strategic technical programs with CSPs and ODM/OEM partners to deploy NVIDIA AI datacenter platforms. Integrate datacenter tools, operationalize AI factory processes, resolve blockers, manage timelines and cross-functional teams, ensure delivery of customer-specific features, and communicate program status and risks to senior leadership.

Lead development of AI enablement roadmap, build state-of-the-art AI systems, and produce tools, services, and blueprints to accelerate AI adoption across NVIDIA. Collaborate with cross-organizational leaders and external partners to optimize productivity for software, hardware, and other workflows.

Design, build, and maintain infrastructure software for large-scale AI training and inference. Improve efficiency, resiliency, availability, observability, and reliability across distributed AI systems. Develop APIs and tools, analyze failures from application through hardware levels, define reliability metrics, and support NVIDIA AI platforms. The role requires expertise in distributed systems, monitoring, debugging, software engineering practices, and AI infrastructure, with preferred experience in large clusters, RDMA stacks, telemetry, and deep learning frameworks.

Develop go-to-market strategies and positioning for NVIDIA AI platform software. Create technical marketing materials including blogs, tutorials, webinars, white papers, presentations, and sample applications. Collaborate with product, engineering, technical marketing, campaign, and PR teams; engage developers and customers at conferences and trade shows; and promote AI training and inference frameworks and related software development kits.

Lead product strategy, roadmaps, and go-to-market plans for AI training and post-training tools, SDKs, and libraries on NVIDIA GPUs. Partner with researchers, production model builders, customers, leadership, and marketing to improve large-scale performance, resilience, and developer experience. Drive products supporting distributed systems, GenAI, machine learning, and open-source frameworks.

Owns end-to-end architecture and implementation of high-performance communication and memory-management libraries for distributed AI. The role drives GPU, DPU, NIC, and switch hardware-software co-optimization; profiles data movement across memory, storage, and networks; integrates networking into AI serving frameworks; contributes to open source; mentors engineers; leads design reviews; and prototypes emerging technologies.

Develop and promote NVIDIA world models for autonomous vehicles through technical content, benchmarks, demos, workshops, developer engagement, and partner enablement. Design and post-train foundation models, prototype generative AI systems, explain technical concepts to business and developer audiences, and collaborate with research, software, product, sales, and customer teams. Serve as an autonomous-vehicle world-models expert while advancing thought leadership, adoption, and deployment of NVIDIA AI platforms.

Lead GPU and NVLink-based cluster design and validation for large-scale AI and HPC deployments. Advise cloud partners on architectures, perform performance modeling, debug deployment issues, support NPI rollouts, and relay field feedback to engineering.