Top Artificial Intelligence Jobs

Lead the strategy for AI analytics products, building AI-powered workflows for performance diagnostics and insights. Collaborate with teams to enhance data-driven decision-making and product health monitoring.

This listing collects unsolicited applications into a talent pool for future openings at Eridu AI. Candidates submit their information; the company will contact applicants if a role matches their skills and interests.

Develop, train, fine-tune, deploy, and monitor large-scale ML models (LLMs, speech, TTS, multimodal). Collaborate with cross-functional teams, run extensive experiments and evaluations, influence product strategy, and mentor engineers to deliver scalable, high-quality AI solutions.

Build and deploy ML solutions for Duolingo Video Call, including LLMs, speech recognition and TTS. Lead model training, fine-tuning, RL, evaluation, deployment, monitoring, cross-functional collaboration, and mentorship to improve learning experiences at scale.

Build, run, and improve AI-driven automations for FP&A, tax, and finance operations. Connect ERP, spend, equity, HRIS, and CRM systems to LLM-powered agents, maintain auditable workflows, and expand the finance team's AI capabilities while supporting forecasting, budgeting, reporting, and month-end close.

The AI/Machine Learning Engineer will develop systems for automating health benefits decisions, integrating large language models with rule-driven patterns, and ensuring compliance with regulations.

Seeking an AI Research Engineer to design AI training protocols, build scalable systems, and collaborate on novel AI infrastructure solutions.

Gradient Network seeks an AI Researcher to conduct original research in distributed ML, model integrity, and incentive design, working with engineering teams and engaging with the global research community.

Design and implement AI-driven test automation and performance engineering for cloud-native, distributed systems. Build CI/CD-integrated automation frameworks, use ML/LLM techniques for test generation and prioritization, establish performance baselines and observability-driven validation, and mentor teams within SAFe delivery trains to embed quality across the SDLC.

Own and architect the company's EPM (Pigment) to drive planning, forecasting, and reporting. Translate business requirements into scalable financial models, lead AI adoption for commentary and anomaly detection, automate finance workflows and conversational agents, and collaborate with Data Science and IT to deploy governed, auditable solutions that streamline FP&A processes for a growing SaaS organization.

Identify and qualify new nonprofit partnership opportunities, conduct outreach via calls, email and social, collaborate with Account Executives, refine outreach playbooks, and support early-stage sales pipeline development.

Design, prototype, and productionize Generative AI and LLM features across backend and frontend. Integrate ML models, fine-tune, perform prompt engineering, operate inference platforms, and support AI-based product launches while participating in on-call duties.

Manage full sales cycle to grow nonprofit customers, build relationships with decision-makers, close deals, expand accounts, share market insights, and hit revenue targets.

Lead presales solutioning for Data and AI opportunities using Snowflake, design architectures, conduct workshops, and drive AI-infused data solutions.

Manage back-end operations of an AI-powered autonomous fundraising system, including human-in-the-loop review, AI training, knowledgebase development, documentation, onboarding, basic website/content support, third-party platform coordination, JIRA issue reporting, and customer troubleshooting.

Manage full sales cycle to acquire and grow nonprofit customers, build relationships with decision-makers, close SaaS deals, share market feedback, and hit revenue targets while supporting existing accounts.

Manage a book of nonprofit customers to drive adoption, retention, renewals, and upsell. Proactively communicate, document engagements, oversee onboarding, analyze usage, execute renewal strategies, and collaborate cross-functionally to improve processes and customer outcomes.

Manage initial implementation and onboarding of new customer accounts focused on AI agents. Coordinate integrations, configure AI agents, conduct full-team trainings, monitor adoption and health, resolve issues, manage escalations during first 30 days, and hand off to CSMs. Build and refine internal onboarding processes.

The Principal AI Learning & Enablement Partner will drive AI adoption across Match Group by creating an AI learning roadmap, partnering with leaders, and enabling employees. They will create practical applications of AI in the workplace and track usage and engagement for impactful outcomes.

Lead and oversee the development and operations of AI infrastructure orchestration, focusing on team management, strategic vision, and technology integration.

As a Senior Hardware Systems Engineer, you will lead AI rack and cluster infrastructure designs, manage deployments, and collaborate with cross-functional teams to optimize data center solutions.

As a Senior Software Engineer, design and develop automation solutions leveraging AI, integrate AI-driven components, and maintain backend systems while collaborating cross-functionally to improve workflows.

Owner of company-wide applied AI initiatives: discover high-leverage workflows, buy or build LLM/agent solutions, ship and operate production integrations, ensure security-by-design, drive adoption, and maintain/sunset tools with clear operational ownership.

Lead AI architecture and strategy for enterprise AI transformation: design scalable platforms using LLMs, RAG, agentic AI, and advanced analytics; establish patterns, governance, and MLOps; guide teams from POC to production while aligning technical roadmaps with business objectives.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.