Top Artificial Intelligence Jobs

Build and govern the enterprise AI context and knowledge layer, including knowledge graphs, semantic retrieval, RAG patterns, connectors, and agent integrations. Design and maintain AI agents, workflows, evaluation practices, and self-service discovery tooling across Slack, Google Workspace, Glean, and AI assistants. Ensure secure, scalable, reliable implementations through access controls, monitoring, incident response, documentation, and cross-functional collaboration.

Leads and develops the sales team, coaches SDR and Account Executive performance, builds repeatable sales processes, manages the full sales cycle, conducts demos, closes business, maintains HubSpot pipelines, reports on performance, and partners with Marketing and Product to improve lead generation, automation, and customer feedback.

Build integrations that enable AI agents to interact with first-party and third-party applications. Develop structured agent actions, information retrieval interfaces, cross-application workflows, reusable SDKs, and reliable asynchronous APIs. Manage authentication, authorization, permissions, and integration limitations while collaborating with Android engineers on system-level application access.

Develop and modify Android system services, privileged APIs, and OS-level AI integrations. Build interfaces for AI agents to interact with Android functionality while managing permissions, IPC, application lifecycles, background execution, performance, and resource consumption. Collaborate with agent engineers and debug Android Framework or AOSP components. Preferred experience includes custom ROMs, AOSP contributions, C++, System UI, background services, and power management.

Design AI-native interaction patterns and proactive experiences for everyday phone use. The role explores voice, touch, conversational, contextual, and multimodal interfaces; creates workflows across applications; makes agent actions transparent and controllable; and prototypes and validates new interaction models. The designer will collaborate closely with product and engineering teams and apply strong usability, product thinking, visual communication, and mobile interaction design skills.

Define the hardware platform strategy and roadmap for an AI-native smartphone. Evaluate mobile SoCs, NPUs, components, power, thermal performance, connectivity, and memory capabilities. Translate product and AI requirements into hardware specifications, assess vendor and ODM proposals, establish validation criteria, and determine whether commercial platforms or custom hardware are needed for current and future on-device AI workloads.

Build agent workflows, orchestration, tool integrations, context retrieval, memory, multi-step execution, and long-running stateful processes. Improve reliability through evaluations and regression testing, integrate local and cloud model providers, and support model routing and inference optimization. The role requires backend or AI application engineering expertise, strong API and distributed systems knowledge, and proficiency in Python, Go, or TypeScript.

Lead Android platform architecture and AOSP-based ROM development for an AI-native phone. Define OS-level AI integration, system services, privileged APIs, permissions, security architecture, and platform extensions. Partner with hardware vendors and ODMs on device integration, evaluate technical trade-offs, and establish Android platform engineering standards. The role requires deep Android internals expertise, ROM development experience, and strong technical leadership.

Integrate AOSP or custom Android ROMs with smartphone hardware, including BSPs, vendor HALs, firmware, drivers, sensors, cameras, audio, modems, Wi-Fi, and Bluetooth. Support device bring-up and boot processes, troubleshoot hardware and firmware compatibility, collaborate with ODMs, OEMs, and chipset vendors, and optimize power consumption and hardware performance.

Evaluate, deploy, and optimize machine learning models for on-device execution on Android phones. Benchmark models on mobile hardware, optimize latency, memory, and power consumption, and apply quantization, compression, and distillation. Integrate models with Android applications, system services, and AI agent runtimes. Build hybrid local/cloud model routing and determine which workflows should run locally versus through cloud inference.

Develop and modify Android Framework components, system services, privileged APIs, and secure AI-agent integrations. The role focuses on application lifecycle, permissions, background execution, first-party app integration, OS stability, performance, and collaboration with agent engineers. Candidates need strong Android development experience, Java or Kotlin proficiency, AOSP knowledge, and familiarity with Binder IPC, permissions, and complex Android debugging. Custom ROM, C++, Linux, Android security, power management, and system-level shipping experience are preferred.

Build and improve LoRA and full fine-tuning workflows for LLMs on GPU infrastructure deployed on AKS. Own dataset ingestion, transformation, validation, and training pipelines while maintaining shared configuration and logging contracts. The role requires strong Python and PyTorch experience, hands-on LLM fine-tuning, CUDA/GPU debugging, and data pipeline development. Preferred experience includes vLLM, Azure Blob Storage, Snowflake, and Kubernetes.

Build and scale AI agents for sales enablement across frontend and backend systems. Lead development with Next.js, React, TypeScript, Python, Postgres, OpenAI, and FastAPI; design robust AI integrations, engage customers, contribute to roadmap planning, and iterate rapidly in a high-ownership startup environment.

Analyze customer support workflows, identify automation opportunities, and build scalable AI-driven solutions. Optimize prompts, monitor integrations and APIs, troubleshoot workflow failures, and improve customer satisfaction. Lead cross-functional automation initiatives, drive adoption across support teams, manage an automation roadmap, and measure operational impact and ROI.

Designs and leads customer-facing cloud and AI application engagements on Microsoft Azure, from architecture and proof of concept through production deployment. The role delivers secure, scalable, resilient solutions using Azure AI Foundry, AKS, App Service, APIM, GitHub, containers, and serverless technologies. Responsibilities include technical delivery ownership, application modernization, customer workshops, production readiness, escalation support, cloud consumption growth, and collaboration with executives, developers, engineering teams, and Microsoft product groups.

Drive technical sales and customer adoption of Microsoft’s cloud and AI platforms through demonstrations, proofs of concept, hackathons, and architecture workshops. Design secure, scalable AI and cloud-native application solutions, resolve technical blockers, and guide production deployments. Build relationships with developers and platform leaders while maintaining expertise in AI Foundry, Azure AI, responsible AI, application modernization, and AI management and security.

Build retrieval and agent systems for Scuba, helping AI agents find and apply relevant team knowledge. Develop evaluation systems grounded in real-world tasks to improve agent performance. This is a full-time, in-office position requiring work five days per week in New York’s West Village.

Conduct end-to-end applied research and engineering to improve web data quality, scalability, and readiness for Search, grounding, and LLM training. Develop machine learning, statistical, data mining, and LLM-based methods; analyze web-scale data; design experiments; apply prompt engineering for data evaluation and labeling; and translate research findings into product decisions and technical roadmaps. Collaborate across teams to establish scalable, responsible, privacy-conscious AI practices.

Leads strategic enterprise sales pursuits involving Dynamics 365, Copilot, and agentic AI. Shapes competitive positioning, business value, solution strategies, technical validation, and customer decision criteria. Coordinates account teams, engineering, product, marketing, partners, and field stakeholders to remove blockers and advance complex opportunities. Develops competitive intelligence, enables sellers through coaching and readiness programs, and translates AI and business application capabilities into measurable customer outcomes, revenue, adoption, and usage.

Leads governance for a complex data science transformation portfolio, ensuring delivery discipline, risk management, reporting, dependencies, benefits tracking, and senior stakeholder alignment. Oversees programme hygiene, governance forums, portfolio prioritization, business cases, investment planning, and executive reporting. Uses technical understanding of data science, machine learning, and AI engineering to challenge plans and assess delivery confidence. Partners with Finance and transformation leaders to validate assumptions and track realized value.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Lead enterprise AI adoption by developing rollout plans, training programs, workflow playbooks, automation use cases, tool governance, acceptable-use policies, and risk controls. Measure adoption and business outcomes, manage an AI champions network, supervise a team, and partner with Security, Legal, Compliance, IT, Procurement, Product, and Engineering. The role focuses on safe, practical AI use across healthcare operations while addressing PHI, retention, vendor data use, HIPAA, and SOC 2 requirements.

Develop and optimize AI models for voice processing, natural language understanding, and speech synthesis. Integrate conversational AI into a Voice AI platform, process large datasets, and deploy scalable models on cloud or on-premise infrastructure. Collaborate with product, engineering, and design teams while testing, debugging, and refining AI solutions for reliable, human-like interactions.

Leads governance for a complex data science transformation portfolio, ensuring programme quality, delivery assurance, coherent workstreams, executive reporting, risk management, benefits tracking, and investment prioritization. The role challenges technical plans across data science, machine learning, and AI initiatives, manages governance forums, supports business cases and financial modeling, and translates technical complexity into clear decisions for senior stakeholders.

Supports PwC tax professionals with corporate tax returns, compliance documents, tax research, international and multistate tax projects, data analysis, and client-focused tax strategy development. The intern will serve entrepreneurial and private businesses through the Japanese Business Network while gaining exposure to tax consulting, regulatory requirements, and tax reporting processes.