Top Artificial Intelligence Jobs

Evaluate and annotate AI-generated responses, write prompts and expected outputs, provide structured feedback, maintain high-quality training datasets, and collaborate with engineers to improve LLM performance and evaluation workflows.

Design, build, and scale enterprise AI solutions including LLM, RAG, and AI Agent systems. Develop cloud-native architectures (Azure/AWS), integrations, MLOps/CI/CD, model monitoring, and governance. Collaborate with stakeholders to translate business needs and drive AI adoption with measurable outcomes.

The Internal Systems Engineer builds AI-powered tools and maintains a shared agent platform while integrating AI into customer operations at impact.com.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Audit and ensure quality of data labeling outputs, coach and onboard Data Labeling Associates, provide actionable feedback, monitor workflows, identify patterns and edge cases, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and review data labeling outputs, provide actionable feedback, train and support labeling teams, monitor workflows, identify recurring issues and edge cases, participate in calibrations and stakeholder syncs, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and validate data labeling outputs, provide structured feedback and hands-on training to annotators, monitor workflows and escalate issues, help apply and improve guidelines, and identify patterns in human and model outputs to maintain high-quality AI training data.

Evaluate AI outputs, audit model behavior, apply and refine labeling guidelines, identify failure modes and trends, document findings, and collaborate with cross-functional teams to improve model evaluation and data quality.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, enforce and improve guidelines, identify recurring issues and edge cases, escalate risks, and document feedback to support human-in-the-loop AI workflows.

Evaluate AI model outputs, perform audit-based reviews, identify failure modes and trends, apply and improve guidelines, document findings, and collaborate with leads and cross-functional teams to refine quality standards.

Evaluate AI model outputs, perform audit-style reviews to find patterns and failure modes, apply and refine labeling/evaluation guidelines, document findings, communicate insights to leads and stakeholders, and participate in team calibrations to improve model evaluation and data quality.

Audit and ensure quality of labeled data by reviewing DLA outputs, providing feedback and coaching, supporting onboarding and training, monitoring workflows, identifying recurring issues and edge cases, and contributing to guideline and process improvements in human-in-the-loop AI workflows.

Audit and improve data labeling quality in human-in-the-loop ML workflows. Coach and onboard labelers, provide actionable feedback, monitor queues, identify recurring issues, and help refine guidelines and processes.

Evaluate AI outputs and audit model behavior, apply and refine guidelines, identify failure modes and trends, document findings, and collaborate with leads and cross-functional teams to improve evaluation frameworks and data quality.

Evaluate AI model outputs, perform audit-based reviews, apply and refine guidelines, identify performance trends and failure modes, document findings, and collaborate with cross-functional teams to improve model evaluation and data quality.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, apply and refine guidelines, monitor workflows and escalate issues, document findings, and support human-in-the-loop AI evaluation processes.

Audit and monitor data labeling outputs, provide actionable feedback, support onboarding and training of annotators, enforce and improve guidelines, escalate workflow issues, and document findings to maintain high-quality human-in-the-loop AI data processes.

Lead end-to-end delivery of AI and data projects by defining scope, timelines, success metrics, coordinating cross-functional teams, managing sprints and backlogs, mitigating risks around data/model quality and compliance, tracking KPIs, and ensuring stakeholder alignment and documentation for production deployments.

Build and ship end-to-end product features, debug and resolve production issues, partner with Product on UX and direction, use AI heavily for development and prototyping, and iterate quickly across the stack to improve quality and speed.

Owner of consumer product areas (creation, discovery, feed, sharing, retention). Turn ambiguous user problems into hypotheses, define metrics, analyze funnels/engagement, work with engineering/design/data to ship experiments, and use AI tools to accelerate research, analysis, prototyping, and execution in a fast startup environment.

The role involves building AI-driven e-commerce systems, managing Shopify operations, integrating with NetSuite, and driving revenue through innovative marketing automation.

Design, build, and ship end-to-end AI features: implement multi-step agentic flows with LangChain/LangGraph, integrate LLMs, connect to live data sources, deploy and monitor AI services on cloud, and instrument observability. Optionally build React/TypeScript front-end components for streaming AI outputs and co-pilot experiences.

Lead end-to-end onboarding for enterprise customers: run discovery, design solutions, configure systems, build integrations, manage escalations, adopt AI-driven workflows, and drive go-lives ensuring fast customer value.

The Associate Director of Research Administration ensures financial management of research projects, oversees staff, develops policies, and maintains compliance with regulations, fostering a high-quality service environment.

Lead technical delivery of AI-enabled web apps, dashboards, and automation. Build full-stack systems (frontend, backend, infra), integrate LLMs, RAG, vector DBs and third-party services, prototype then productionize solutions, work client-facing for scoping and translation, and provide technical leadership across architecture, standards, and documentation.