Top Artificial Intelligence Jobs

Generate and qualify pipeline among AI compute buyers through personalized outbound, events, referrals, and market research. Qualify needs including workloads, GPU requirements, token volume, latency, region, timing, contract terms, and budget. Maintain CRM records, convert event conversations into meetings, hand off qualified opportunities, support deals through close, and share buyer feedback with leadership. The role requires regular Bay Area travel and occasional travel to U.S. conferences.

Lead Stripe’s North American AI startup sales organization, managing frontline leaders and specialized Account Executives. Develop go-to-market strategy, coverage models, forecasting systems, and AI-focused ecosystem partnerships. Drive adoption of usage-based billing, Connect, Financial Connections, Global Payouts, and other products. Recruit and develop sales leadership, identify breakout AI companies, shape product roadmaps, and accelerate acquisition, expansion, and cross-sell across high-growth AI startups.

Leads end-to-end multimodal AI data projects, managing scope, timelines, budgets, staffing, client communication, workflows, annotation guidelines, quality metrics, and delivery risks. Translates technical requirements into collection and annotation processes, troubleshoots data issues, develops SOPs and QA frameworks, advises on feasibility and tooling, and mentors operations specialists. The role requires experience with AI data operations or project coordination and familiarity with image, video, 3D, sensor, or robotics workflows.

Lead Voltify’s finance strategy across equity fundraising, project finance, tax equity, lease facilities, infrastructure partnerships, deal design, financial modeling, public-company readiness, incentives, treasury, tax, legal coordination, and payments. Build a lean finance organization and establish scalable funding structures for battery locomotives, charging infrastructure, and microgrids.

Leads the technical vision, architecture, roadmap, integrations, AI evaluation, and operational readiness for an AI-powered insurance submission ingestion platform. Translates underwriting and business needs into technical requirements, establishes platform standards, manages trade-offs, defines quality and release criteria, coordinates regional rollouts, validates production readiness, and partners with governance teams. The role requires deep expertise in engineering, platform architecture, AI/ML systems, APIs, event-driven design, cloud platforms, and regulated enterprise delivery.

Leads AI engineering teams building and scaling production-grade agentic AI applications, multi-agent systems, LLM orchestration, and intelligent workflows. Oversees Databricks on AWS platform operations, architecture, security, and optimization. Establishes engineering standards for Python development, testing, CI/CD, and DevOps/DataOps, while partnering with Product teams on execution. Manages engineering leaders and staff, drives hiring and mentorship, and shapes the organization’s AI technical direction.

Leads end-to-end AI post-training and model evaluation projects, managing scope, timelines, budgets, staffing, risks, client communication, and delivery quality. Translates model improvement goals into data requirements, workflows, rubrics, evaluation plans, and acceptance criteria. Advises on preference data, model comparisons, quality standards, and feasibility; develops SOPs and QA frameworks; resolves ambiguous requirements; mentors operations specialists; and supports project scoping and new capability development.

Leads the enterprise AI product portfolio, including a unified AI Core Platform with LLM gateways, retrieval systems, and agentic frameworks. Manages and mentors Product Managers while partnering with Data Science, Engineering, UX, Legal, and Governance teams. Establishes AI product methodologies, evaluates use cases, drives digital transformation, and governs ethical deployment, privacy, security, bias mitigation, and human oversight. Uses telemetry, SQL, and visualization tools to measure model performance, costs, and productivity.

Lead the design, development, deployment, and optimization of agentic AI, LLM, deep learning, and propensity modeling solutions. Build models predicting customer behavior, churn, and lifecycle triggers; manage end-to-end pipelines and production monitoring; provide technical mentorship; and communicate model insights and recommendations to senior leaders and business partners.

Build integrations, automation, analytics pipelines, dashboards, and self-service tools for enterprise workforce AI platforms including Microsoft Copilot, Claude, and Glean. Develop adoption and usage reporting, automate platform administration and licensing governance, support AI enablement programs, and integrate privacy, security, and responsible AI practices. Lead complex projects, mentor developers, collaborate with stakeholders, demonstrate solutions, and recommend technology and process improvements.

Sells complex HPC and AI infrastructure products, services, and software across the Southeastern United States. Builds pipeline, develops new opportunities, expands customer relationships, engages executive and technical stakeholders, creates business cases, negotiates and closes deals, and partners with account and solution teams. Serves as the regional HPC and AI subject matter expert while growing revenue and market share.

Leads the Operations team, manages coordinators, and oversees orders, provisioning, warranties, licensing, inventory, supply chain, vendors, and business systems. Drives process improvements, cost efficiency, data integrity, product launches, end-of-life transitions, technology alliance programs, and pre-sales deployment activities. Partners across departments and manages escalations while maintaining global manufacturer and vendor relationships.

Advises product, engineering, partnerships, sales, marketing, and executives on AI product legal matters. Provides guidance from product design through launch, negotiates data partnerships, IP and model licensing, SaaS customer contracts, APIs, DPAs, reseller, procurement, marketplace, vendor, and strategic partnership agreements. Builds scalable legal playbooks, self-service resources, enablement programs, and AI-enabled automation while monitoring evolving AI law and policy.

Conduct research on next-generation tiered memory architectures for AI inference and large language model workloads. Develop models, simulations, prototypes, and performance evaluation tools; investigate memory hierarchy, data placement, caching, and migration strategies. Analyze hardware, software, and system-level tradeoffs using hardware platforms, simulation environments, and FPGA prototypes. Document findings, present technical results, and collaborate with architecture teams on future memory technology strategies.

Build and lead development of Ark, an AI-native software development and secure delivery product. Responsibilities include creating AI agent workflows, isolated execution environments, Kubernetes and UDS integrations, persistent asynchronous systems, automated testing and validation, and cloud-native deployment tooling. The role involves architecture decisions, complex debugging, reliability and security improvements, code reviews, mentoring, and collaboration with Product, UX, and mission-focused customers.

Evaluates and compares chatbot responses against business requirements and quality criteria. Gathers requirements, documents functional specifications, analyzes workflows and data, creates reports and dashboards, supports testing and UAT, tracks project risks and KPIs, and presents evidence-based findings. Collaborates with technical teams and stakeholders to improve AI output quality while maintaining evaluation consistency and meeting deadlines.

Operate and improve enterprise AI/ML platforms across Palantir Foundry, AWS Bedrock, SageMaker, and cloud-native services. Responsibilities include MLOps automation, deployment, monitoring, observability, governance, incident response, root cause analysis, reliability engineering, and lifecycle management for production AI workloads. The role also provides technical leadership, mentorship, and cross-functional collaboration while supporting Generative AI, LLM, and RAG solutions.

Leads systems software prototyping and workload characterization for Azure hardware/software co-design. The role optimizes AI inference and LLM workloads, especially KV Cache placement, migration, and utilization across GPUs, CPUs, CXL memory, storage, and networks. Responsibilities include developing inference framework extensions, instrumentation, performance models, simulation frameworks, and memory-tiering proof-of-concepts while influencing long-term architecture and collaborating with hardware, software, workload, and industry partners.

Designs, develops, and supports secure AI-powered applications, agents, APIs, integrations, and workflow automations. Builds Generative AI solutions using LLMs, prompt engineering, retrieval patterns, and enterprise data. Responsibilities include full software delivery lifecycle participation, AI output evaluation, stakeholder collaboration, secure engineering, governance, monitoring, risk controls, and operational readiness.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.

Build and deploy enterprise agentic AI solutions, including agents, MCP tools, plugins, orchestration architectures, evaluation harnesses, and secure APIs. Integrate agents with search and collaboration platforms, instrument production behavior, manage cost and latency, and test against prompt injection and tool vulnerabilities. The role requires backend software engineering, production agent development, MCP experience, LLM evaluation, and AI coding assistant expertise, with preferred experience in managed runtimes, retrieval systems, and enterprise security.

Lead development and deployment of an agentic AI/ML pipeline for identifying, prioritizing, and pricing high-cost healthcare claims. Responsibilities include model development, confidence scoring, HITL safety design, ML lifecycle management, monitoring, workflow automation, responsible AI, and enterprise-scale analytics using Databricks and Snowflake. The role also evaluates emerging AI technologies and supports regulatory-aware automation and healthcare data transformation.

Designs, deploys, and scales AI and machine learning solutions for enterprise healthcare applications. Responsibilities include building and tuning production models, developing MLOps frameworks and deployment pipelines, monitoring model performance, automating workflows, evaluating emerging AI technologies, and ensuring security, scalability, responsible AI, and governance standards. The role collaborates with software engineers, data scientists, and business stakeholders.

Architect and build production-grade AI platforms and autonomous security agents for vulnerability discovery, validation, and remediation. The role focuses on agentic orchestration, LLM and generative AI integration, AI red-teaming, model deployment, MLOps, observability, evaluation, safety, and scalable APIs. The engineer will partner across Product, Security Research, and Engineering while advancing responsible AI capabilities.