Top Artificial Intelligence Jobs

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 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.

Develops and maintains application software and system components; monitors and administers applications; provides technical and application support; ensures data integrity and security; may work with vendor software and manage small projects.

Design, integrate, and deploy AI/ML systems for defense products: develop inference services, data pipelines, model evaluation and robustness testing, prototype to production, and optimize edge/GPU deployments while collaborating with product and mission teams.

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.

SoftSnow is seeking an AI Ethicist to define ethical guidelines, assess risks, and ensure compliance in AI implementations across various industries.

Design and build the intelligence layer: retrieval and memory systems, RAG pipelines, feedback and evaluation loops, RL and fine-tuning experiments, LLM integration, observability for AI performance, and collaborate to productize AI features.

Lead production agentic development and build R&D tooling that accelerates team workflows. Ship weekly in the main codebase, create and improve custom agents, MCP integrations, evals, and codegen pipelines, train engineers on new workflows, and raise team standards for prompts, tooling, and autonomous throughput.

Own and build Tern's AI and data systems end-to-end: data pipelines, production AI services, evaluation harnesses, monitoring and reporting. Ensure data quality across ingestion, modeling, and reporting, ship reliable production software, and collaborate with product squads to deliver trustworthy AI features and advisor-facing analytics.

Lead AI initiative lifecycle and integration across enterprise platforms. Assess AI project feasibility and ROI, document workflows and data flows, design integration with Salesforce/FIT/RPA, coordinate cross-functional stakeholders, perform pre/post-deployment testing and monitoring, and establish standards and operational frameworks to ensure adoption and performance.

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.

Evaluate AI outputs, perform audit-based reviews, identify patterns and failure modes, apply and improve annotation/evaluation guidelines, document findings, and communicate insights to cross-functional teams to improve model performance and evaluation frameworks.