Top Artificial Intelligence Jobs

Conduct hands-on research involving AI hardware, semiconductor processes, optics, machine vision, and mechanical systems. Build and program opto-mechanical 3D vision systems, automate data acquisition and image reconstruction, develop ETL and database solutions, and analyze advanced AI products. Collaborate with engineers on goal-oriented projects while developing scientific programming, automation, semiconductor processing, and AI architecture expertise.

Build and maintain AI networking models covering datacenter architectures, optics, switching, interconnects, and component economics. Analyze supplier disclosures, technology transitions, costs, power, cooling, and supply-chain constraints. Develop three-statement financial models and forecasts, write research reports, prepare client deliverables, attend conferences, and build relationships with vendors, hyperscalers, and technologists.

Supports research on AI infrastructure, GPU cloud providers, AI model developers, and AI economics. Responsibilities include collecting and validating company, pricing, capacity, and funding data; maintaining source records; building spreadsheet models; checking calculations; and drafting research notes. The intern will analyze AI compute demand, token economics, cloud total cost of ownership, and related technology markets under analyst guidance.

Research AI infrastructure and economics across GPU cloud providers, AI model developers, and AI tokenomics. Build company datasets, reconcile conflicting evidence, maintain financial and unit-economics models, analyze disclosures and technical materials, conduct interview research, draft research notes and charts, and support consulting projects with market and financial analysis.

Generate and develop qualified sales leads for Talkspace’s AI-powered product through inbound and outbound strategies. Conduct personalized email, phone, and social outreach; research prospect needs; coordinate discovery meetings with sales representatives; and consistently exceed sales targets and quotas. The role requires CRM and outbound sales tool proficiency, strong interpersonal skills, initiative, adaptability, and interest in mental health and emerging AI technology.

Research and develop AI solutions for complex energy-sector challenges, spanning machine learning, deep learning, generative AI, and foundation models. The role involves theoretical and empirical research, prototyping, scalable software implementation, collaboration with internal and external researchers, patent creation, publishing, and grant support. Projects may address power generation, renewable energy, electric grids, robotics, and manufacturing.

Leads program management for Cisco Networking’s AI transformation and Design Foundations initiatives. Builds roadmaps, milestones, governance, risk and dependency tracking, reporting, and decision-making processes. Partners across design, product, engineering, research, security, AI, and operations teams to scale AI-assisted design practices, establish adoption standards, and move initiatives from experimentation to durable organizational capabilities. Drives measurable outcomes, cross-portfolio alignment, enablement, and change management.

Leads AI engineering teams delivering AI-enabled workflow solutions across investment systems. Oversees smart automation, agentic AI, RAG, orchestration, evaluations, observability, guardrails, and responsible AI controls. Partners across product, engineering, data, cybersecurity, risk, compliance, and operations to translate strategy into MVPs, scale human-plus-agent capabilities, improve processing and exception management, and manage solution roadmaps, stability, and continuous improvement.

Develop and evaluate machine learning and LLM-based systems, including neural network tuning, model development, benchmarking, experiment design, and performance validation. Use Python or R, deep learning frameworks, SQL, GPUs, and version control systems. Preferred work includes Hugging Face, cloud architecture, semantic search, Tableau, Explainable AI, ONNX, and web development. Requires U.S. citizenship and an active TS/SCI with Polygraph clearance.

Supports an enterprise sales pod by researching target accounts, identifying stakeholders, creating AI-assisted outbound campaigns, booking qualified meetings, and improving follow-up and opportunity progression. Builds AI agents, workflows, automations, dashboards, and GTM assets to increase account prioritization, personalization, engagement, pipeline, and ARR. Partners closely with enterprise account executives and contributes beyond initial meeting generation.

Leads the design, development, testing, deployment, and architecture of production-grade LLM and agentic applications. Builds retrieval, tool-calling, orchestration, evaluation, guardrail, monitoring, and observability systems. Establishes AI-assisted development standards, reviews AI-generated code, mentors engineers, diagnoses model and prompt issues, and collaborates cross-functionally on AI strategy and product delivery.

Build and improve production document-intelligence systems for commercial insurance using parsing, OCR, classification, structured extraction, reconciliation, retrieval, summarization, and deterministic logic. Evaluate model and parser behavior, investigate failures, maintain evaluation sets, compare technical approaches, collaborate with domain experts, and ship reliable Python and Azure Functions services with strong testing, observability, security, cost, and latency considerations.

Lead the strategy, delivery, and continuous improvement of data and AI platforms and products. Translate business goals into requirements, user stories, backlogs, and measurable KPIs. Coordinate agile cross-functional teams, ensure quality and adoption, manage client and executive stakeholders, surface risks, and drive alignment. The role owns product workstreams within consulting engagements and requires strong data and AI platform knowledge, consulting experience, and significant end-to-end product ownership.

Develop and characterize ReRAM technology, including device behavior, variability, reliability, modeling, and array analysis. Build AI-enabled engineering solutions using machine learning, generative AI, physics-informed AI, and AI agents for semiconductor modeling, simulation, EDA automation, data analysis, and workflow improvement. Collaborate across semiconductor engineering teams and translate technical challenges into practical software, modeling, and automation tools.

Lead the design and engineering of an AI-native experience layer for service operations. Build real-time operational visualizations, agent-to-agent interfaces, APIs, and tools that make automated decisions visible and trustworthy. Own UI/UX, information architecture, prototyping, production delivery, technical direction, and mentoring. Partner with AIOps and operations teams to support incident response through interactive telemetry, topology, alert, timeline, and predictive-signal experiences.

The Associate Director will monitor and analyze firmwide AI usage, identify adoption barriers and capability gaps, and translate findings into enablement strategies. The role partners with technology, professional development, and IT teams on analytics, training, support, and responsible AI compliance. It also builds relationships across legal practices and business services to identify practical AI use cases, improve adoption, and inform broader solutions and communications.

Translates legal and business needs into practical AI solutions, partnering with attorneys, business services, IT, and knowledge teams. The role designs workflows, builds solutions using approved AI platforms, evaluates emerging legal technologies, manages supplier relationships, measures adoption and ROI, and advises firm leadership on AI strategy. It also supports attorney and client-facing AI communications and monitors developments across the legal AI market.

Develop and execute enterprise AI and data strategies for clients, including AI value cases, operating models, talent strategies, technology roadmaps, responsible AI plans, and commercialization opportunities. Build trusted C-suite relationships, lead transformation workstreams, shape new business opportunities, and guide large-scale AI-enabled reinvention initiatives. The role requires consulting experience, strong program management and communication skills, leadership capability, and up to 80% travel.

Leads AI governance, risk assessment, security, privacy, and compliance programs for generative and agentic AI systems. Coordinates cross-functional teams, reviews customer and regulatory requirements, establishes AI agent management structures, automates governance controls, manages technical improvement projects, supports audits, delivers training, and reports risks and metrics to senior leadership. The role requires extensive cybersecurity experience, AI security and threat modeling expertise, framework compliance, project management, and executive communication.

Designs and deploys production AI solutions for renewable energy asset development and operations. Responsibilities include architecting RAG systems and agentic workflows, building ML models for forecasting and predictive maintenance, integrating LLM APIs, developing prototypes, implementing MLOps pipelines, scaling proofs of concept, and establishing monitoring, governance, and security practices. The role also gathers requirements, presents solutions and ROI, and trains stakeholders.

Leads end-to-end data engineering solutions, including scalable Snowflake and dbt pipelines, data modeling, SQL transformations, CDC, ELT, quality frameworks, monitoring, and production deployment. Designs engineering standards, troubleshoots complex data issues, improves performance and cost efficiency, collaborates with stakeholders, leads technical reviews, and mentors engineers.

Own AI products and agentic solutions from business discovery through prototyping, acceptance testing, deployment, adoption, and value measurement. Design agent workflows, human-in-the-loop controls, requirements, roadmaps, and responsible AI safeguards. Collaborate with engineers, consultants, clients, and risk specialists to build practical solutions, validate behavior, support change, and improve product outcomes.

Administer enterprise AI platforms, including access controls, API keys, configurations, monitoring, and observability. Build and maintain Python or JavaScript/TypeScript integrations, scripts, automations, REST API connections, vector-store pipelines, and retrieval workflows. Troubleshoot production issues, manage deployments and documentation, monitor resource usage, and collaborate with DevOps, Data Engineering, and InfoSec teams on reliability, security, and compliance.

Build and ship LLM- and agent-based prototypes and production solutions. Develop traditional ML where appropriate, prioritize reuse and scalability, collaborate with product, platform, and frontend partners, and support business development with technical credibility.

Build AI-powered development tools, intelligent agents, automated workflows, and full-stack platform services. Develop React, JavaScript, and TypeScript frontends alongside C# and .NET Core backend services and REST APIs. Create automated testing and validation workflows, integrate AI solutions into engineering processes, and improve productivity, reliability, and scalability. Collaborate with globally distributed teams while using tools such as Claude Code to streamline coding, debugging, documentation, and operational tasks.