Top Artificial Intelligence Jobs

Own and grow a portfolio of GenAI startup accounts by identifying prospects, leading technical conversations, coordinating onboarding and integrations, shaping tailored solutions and proofs of concept, and finding expansion opportunities through usage data. The role requires credibility with founders, engineers, and executives, plus the ability to explain AI infrastructure topics such as inference, APIs, model tuning, and GPU architecture. The Account Executive also represents Fireworks across the GenAI ecosystem and translates customer feedback into product insights.

Own the AI platform product roadmap for humanoid robot autonomy, defining reusable robot capabilities, data and feedback workflows, and customer-facing interfaces for deployment, monitoring, diagnostics, and operations. Partner with research, engineering, and commercial teams to translate AI advances into products, understand customer use cases, and prioritize opportunities. Regular customer-site visits across Europe and beyond are required, up to 10%.

Build reinforcement learning environments to evaluate AI systems on software engineering tasks involving MCP tools. Responsibilities include creating reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature development, codebase refactoring, and performance optimization. The role is remote, flexible, contract-based, and output-focused, with no prior AI experience required.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Lead architecture and implementation of a patient-controlled health data platform: custody, consent management, secure EHR integrations, AI-native workflows (LLMs, agents, RAG), mobile-first patient experiences, and build the engineering culture and hiring bar as a founding technical partner.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, and evaluation. Collaborate with data and infra teams, drive architecture and inference decisions, and provide technical leadership and mentorship.

Owns enterprise customer deployments from discovery and workflow design through execution, go-live, and long-term success. Leads customer discovery, translates business processes into AI agent workflows, coordinates with delivery engineers and internal teams, configures and tests agent solutions, and manages deployment plans, risks, timelines, scope changes, escalations, and executive communications. The role also improves scalable onboarding processes and requires significant client interaction and travel.

Own customer deployments from discovery and workflow design through execution, launch, and ongoing success. Lead customer discovery, translate business processes into AI agent workflows, configure and test agent setups, manage deployment plans and risks, coordinate stakeholders, lead customer meetings, handle scope changes and escalations, and improve repeatable onboarding processes. The role is client-facing, onsite, early-career, and requires approximately 25–30% travel.

Provides remote executive and operational support for coaching engagements. Responsibilities include tracking client engagements, managing calendars and deliverables, drafting follow-ups, preparing coaching documents, administering business software access, maintaining digital files, and supporting executives. The role also researches and optimizes AI workflows using Claude and Gemini for document generation, transcript analysis, and automation, while coordinating multiple concurrent priorities with minimal supervision.

Participants complete guided AI research activities onsite, including walking, sitting, gestures, photos, audio recordings, and video recordings. The study takes 2.5–5 hours and requires no prior experience. Applicants must be ages 13–75+, speak English, and be authorized to work in the United States. Minors need parental or legal guardian consent.

Design, train, deploy, and optimize deep learning and computer vision models for semiconductor inspection systems. Develop production-quality algorithms combining deep learning with physics-based modeling or signal processing, manage datasets and evaluation pipelines, and improve inference performance using GPU and high-performance computing. Collaborate across engineering teams and support deployments at customer sites, including occasional international travel.

Design and implement large-scale, distributed AI-driven SD-WAN systems and real-time, fault-tolerant services. Drive features end-to-end from design through testing and deployment, improve scalability and operability, participate in code and design reviews, and collaborate with product and documentation teams.

Lead Americas Procurement digital solutions team to build reporting, automations, systems support, data governance, and AI/ML enablement. Prioritize project intake, deliver Power BI and Oracle-based solutions, drive process automation and adoption, and track business impact while coaching and developing team capability.

The Enterprise Data Architect develops and optimizes database solutions, migrating data, conducting tests, and ensuring information system functionality.

Conduct applied research in generative video and audio synthesis, voice and roomtone cloning, multimodal understanding, computer vision, and speech modeling. Design, implement, test, and evaluate deep learning algorithms, develop new research directions, and help move prototypes into production features. The role requires end-to-end ownership, strong experimental judgment, and the ability to generate and rapidly evaluate machine learning ideas.

Designs, builds, and ships technical growth mechanisms across acquisition, onboarding, referrals, activation, and monetization. Owns growth budgets, tracking specifications, analytics events, experimentation, attribution, and cohort analysis. Prototypes growth hypotheses for AI products, develops business cases and go-to-market strategies, and presents data-driven recommendations to executives, clients, and partners. The role requires strong coding, analytical, communication, executive presence, and end-to-end ownership skills.

Design and scale AI-powered sales enablement experiences: architecture for generative AI, RAG, AI agents, content discovery, guided selling, coaching automation, governance, pilots, integrations with enablement/CRM/LMS systems, and measure adoption and business impact.

Lead design and production of ML/Deep Learning and time-series forecasting models, architect and fine-tune LLM/GenAI solutions, build conversational and agentic interfaces, transition prototypes to scalable production, and provide technical leadership and mentorship across cross-functional teams.

Entry-level data and AI engineering roles for 2027 graduates. Responsibilities may include building cloud data platforms and pipelines, developing AI software and models, creating data quality and governance processes, managing data assets, and supporting data-driven business decisions. Candidates will use programming, analytics, visualization, cloud, and data management skills while collaborating with technical and business stakeholders.

Leads and develops a software engineering team building Libra Solutions’ platforms in an AI-native, agentic delivery model. Owns delivery commitments, technical quality, architecture, code reviews, engineering standards, CI/CD, technical debt, and AI-assisted development practices. Partners with Product, Engineering, and business stakeholders while tracking delivery and system health metrics. Coaches engineers, supports hiring and retention, and establishes responsible AI, prompt engineering, and output-review standards.

Build and lead Replicant’s company-wide AI enablement program. Develop MCP integrations, shared agents, agent identity management, enterprise tool connections, reusable AI workflows, and cost-visibility mechanisms. Partner with Sales, Delivery, Finance, People, Engineering, and other teams to identify opportunities, implement solutions, promote AI literacy, and develop internal champions. Optimize AI systems through model routing, prompt caching, better agent design, and secure infrastructure.

Lead AI strategy and teams responsible for Hinge's recommendation and ranking systems, driving real-time personalized recommendations and generative features that optimize for dating outcomes, fairness, scalability, and measurable user success. Own roadmap, cross-functional partnerships, budget, responsible AI, hiring, and execution cadence.

Build and own production AI/ML systems for private-market data, including agentic document retrieval, extraction and normalization pipelines, validation, audit trails, and portfolio analytics. Shape AI strategy, model selection, evaluation frameworks, and product direction. Collaborate with clients, support customer-facing initiatives, establish engineering standards for secure and scalable B2B SaaS, and mentor future team members.

Build Replicant’s internal AI enablement program by developing reusable agent workflows, MCP integrations, agent identity management, enterprise SaaS connections, and cost visibility mechanisms. Partner with Sales, Delivery, Engineering, Finance, People, and other teams to identify opportunities, implement secure AI solutions, create shared templates and skills, build AI literacy, and develop internal champions. Optimize AI infrastructure through prompt caching, model routing, tool consolidation, and improved agent design.

Generate outbound pipeline by targeting CFOs, Controllers, and senior finance leaders across Latin America. Design multichannel campaigns, use AI and automation for research, personalization, prioritization, and follow-up, and build workflows connecting data, CRM, enrichment, and outreach tools. Manage HubSpot and Amplemarket, test messaging and channels, analyze results, and create repeatable outbound playbooks. Partner with AEs, Sales Leadership, and RevOps to scale the sales engine.