Top Artificial Intelligence Jobs

Leads enterprise strategy, governance, and implementation of Agentic AI across semiconductor new product development. Builds AI-enabled design platforms, reusable engineering agents, knowledge repositories, and governance frameworks spanning analog, digital, verification, layout, testing, packaging, and systems engineering. Drives cross-functional transformation, establishes productivity and quality metrics, oversees AI lifecycle management, and delivers measurable improvements in development speed, engineering efficiency, IP reuse, and design quality.

Lead discovery and product design for Felix’s conversational AI platform, defining generative and declarative UI patterns across WhatsApp, iOS, and Android. Research user behavior, prototype and ship platform experiences, establish conversation design direction, collaborate with product squads and engineers, and integrate compliance, consent, and recovery states into the experience.

Lead cross-product discovery and define Felix’s conversational, generative, and declarative UI platform. Research user behavior, establish design patterns, prototype and ship the initial WhatsApp experience, then expand patterns to iOS and Android. Collaborate with product squads and engineers, guide conversation design, integrate compliance disclosures and consent, and influence product direction through user research, analytics, critique, and hands-on design execution.

Build AI applications, agents, integrations, and automated workflows that transform technical information into customer documentation. Partner with the Documentation team to use LLMs, agentic AI, and automation to improve content creation, maintenance, publishing, and delivery. Reduce manual effort, increase access to technical knowledge, and identify additional customer-facing workflows where AI can improve efficiency and experience.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

Conduct fundamental research in generative AI, designing and implementing novel large-scale models for text, image, video, 3D, multimodal, or scientific applications. Publish original research, collaborate with research and product teams, present at conferences, and transfer innovations to product groups.

Lead development and optimization of local AI agent frameworks and runtimes for Windows PCs with NVIDIA GeForce RTX GPUs. Build secure, sandboxed, privacy-focused agent execution and inference systems, collaborate with AI research, driver, and open-source teams, and establish deployment guidelines. Mentor engineers while delivering production-ready code using Windows systems programming, C++, Python, LLM inference pipelines, GPU acceleration, and agent orchestration frameworks.

Develop AI-driven software systems for VLSI and circuit design, including machine learning models, combinatorial optimization algorithms, and agentic AI workflows. Analyze datasets, validate hypotheses, and build algorithms for circuit and layout optimization, pre- and post-silicon design, and SPICE correlation. Collaborate across engineering and research teams while advancing electronic design automation solutions.

Build and productionize computer vision, 3D perception, and LLM-powered systems using proprietary LiDAR and imagery datasets. Own AI models throughout data preparation, training, evaluation, deployment, and monitoring. Develop agentic workflows, MCP integrations, and AI copilots while creating metrics tied to customer outcomes. Collaborate across hardware, software, and customer teams to rapidly prototype and deliver reliable infrastructure intelligence products.

Own product marketing for Braze Decisioning Studio, including positioning, messaging, launches, competitive strategy, pricing and packaging input, field enablement, and portfolio narrative. Translate complex AI and data technologies for technical and business audiences, influence product roadmaps, engage customers and analysts, and measure impact through pipeline, adoption, expansion, and competitive win rates. This senior individual contributor role requires cross-functional execution, technical credibility, strong writing, and enterprise AI and martech expertise.

Own product marketing for Braze Decisioning Studio, including positioning, messaging, launches, competitive strategy, pricing and packaging input, field enablement, customer insights, and portfolio narrative. The role translates complex AI and data infrastructure concepts for technical and business audiences, influences product roadmaps, partners across Product, Engineering, Sales, and Marketing, and measures success through pipeline, adoption, expansion, and competitive win rates.

Own product marketing for Braze Decisioning Studio, including positioning, messaging, launches, competitive strategy, pricing and packaging input, field enablement, and portfolio narrative. Translate complex AI and data technologies for technical and business audiences, influence product roadmaps, engage customers and analysts, develop thought leadership, and measure impact through pipeline, adoption, expansion, and competitive win rates.

Leads the enterprise FinOps for AI discipline, establishing governance, cost transparency, forecasting, allocation, chargeback, optimization, and reporting across AI platforms and services. Builds AI unit economics, embeds financial guardrails into development workflows, validates savings, and partners with engineering, finance, procurement, security, and business leaders. Communicates AI spending, risks, trade-offs, and investment opportunities to executives while developing methodologies and operating models for evolving AI consumption.

Develop and productionize personalized search ranking, recommendation, and natural-language discovery systems. Apply deep learning, transformer models, graph neural networks, multimodal signals, LLMs, and behavioral data to improve search relevance. Partner cross-functionally on experimentation, model development, agent evaluation, and MLOps. Provide technical guidance through code reviews and best-practice sharing while advancing scalable machine-learning systems for e-commerce discovery.

Design conversational dialogs, prompts, scripts, and interaction flows for virtual assistants, chatbots, and omnichannel digital self-service experiences. Collaborate with product, engineering, research, and business teams in Agile environments; apply user-centered design, research, analytics, and feedback to improve experiences. Work with conversational AI platforms, APIs, datasets, LLMs, NLP, and generative AI technologies while supporting testing, implementation, and measurement under senior designer guidance.

Architects scalable, secure, compliant, and cost-conscious enterprise infrastructure and AI platforms. Defines AWS-first reference architectures, platform engineering patterns, governance, observability, integration, security, and operational guardrails. Evaluates emerging AI and automation technologies, validates designs through proofs of concept, partners with engineering and IT stakeholders, and guides platform implementation and handoff in a regulated healthcare environment.

Build enterprise data models, semantic layers, metadata, SQL transformations, and cloud data pipelines. Improve text-to-SQL and generative BI accuracy by partnering with AI engineers on RAG, agents, prompts, testing, and guardrails. Translate business needs into data requirements, validate KPIs, document schemas, apply quality and governance standards, and collaborate with technical and non-technical stakeholders across pharma, life sciences, manufacturing, and healthcare projects.

Lead hands-on development and delivery of end-to-end AI and GenAI solutions (RAG, vector stores, LLM integrations, ML pipelines). Write production-quality code, design architectures, optimize performance and cost, deploy and monitor models, establish MLOps and governance standards, mentor and manage a small AI development team, evaluate vendors, and help shape enterprise AI strategy and responsible AI practices.

Lead the architecture and development of production-grade AI solutions combining LLMs, full-stack engineering, data platforms, cloud architecture, security, and DevOps. Build AI-powered tools for meeting preparation, call summarization, workflow automation, knowledge retrieval, recommendations, and decision support. Partner with engineering, data, product, and business teams to identify opportunities, scale AI applications, and deliver measurable productivity improvements.

Build and deploy production-grade AI agents, workflow automations, and internal applications that improve enterprise processes. Partner with business, operations, engineering, and security teams to identify use cases, design reliable agent architectures, prototype with LLMs and agent frameworks, measure impact, and drive adoption. The role requires strong systems engineering, API, data, and workflow design skills, along with the ability to translate ambiguous business problems into scalable technical solutions.

Build and productionize computer vision, 3D perception, and LLM-powered systems using LiDAR and imagery to assess infrastructure. Own AI models throughout data preparation, training, evaluation, deployment, and monitoring. Develop agentic workflows, MCP integrations, and customer-facing AI tools while defining performance metrics and evaluating emerging technologies.

Supports the full AI and machine learning lifecycle, including data preprocessing, model development, training, optimization, deployment, monitoring, and evaluation. Develops deep learning, NLP, computer vision, and generative AI models; analyzes large datasets; performs feature engineering; writes production-quality code; researches emerging AI methods; and collaborates with cross-functional engineering, data, product, and project teams.

Build secure data pipelines, relational databases, ETL workflows, and AI/GenAI capabilities for visual analytics in air-gapped and multi-enclave environments. Develop integrations using Python, TypeScript, React, FastAPI, PostgreSQL, Docker, and Kubernetes. Support vector and graph data orchestration, data lineage, access controls, distributed infrastructure, and high-performance analytics. Mentor junior engineers and contribute to technical standards, system optimization, security improvements, and application integration.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Designs, tests, governs, and documents AI-assisted workflows for vulnerability and risk analysis in a federal cybersecurity environment. Responsibilities include structured prompt engineering, model parameter testing, hallucination reduction, prompt library management, quality validation, reproducibility benchmarking, and converting CISA cybersecurity workflows into standardized AI templates using enterprise Microsoft Copilot tools.