Top Artificial Intelligence Jobs

Integrate and maintain commercial AI tools and LLM agents into VLSI design flows, coordinate multi-agent systems, optimize performance and QoR, deploy AI agents and chatbots, preprocess data, evaluate solutions, and train end-users to improve design productivity.

Lead QA strategy and automation for ML/AI-powered services. Develop LLM-assisted test generation, build scalable automation frameworks, validate ML/LLM outputs, debug model and service issues, and integrate testing into CI/CD for distributed systems.

The AI Enablement Engineer will design and build internal tools and AI-powered systems for client deployments, utilizing a full-stack approach. Responsibilities include creating deployment tooling, integrating AI solutions, and operating with high autonomy to identify problems and develop effective solutions.

Lead design and implementation of scalable AI-centric systems for offensive security. Develop agentic AI capabilities for vulnerability identification and automated pentesting, own feature delivery from ideation to production, mentor engineers, run design/code reviews, and collaborate cross-functionally to integrate AI into platform systems.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

As a Senior/Principal Machine Learning Engineer, you'll design and build core ML systems for Workday's AI agents, collaborating with cross-functional teams on models and deployment for real-world applications.

As a Senior Software Engineer at Tenstorrent, you will develop AI compiler technologies, optimize computational graphs, and enhance MLIR frameworks to connect AI models with hardware.

The Technology & AI Learning Specialist delivers technical training programs, facilitates onboarding, collaborates with stakeholders, and designs learning resources for GT attorneys and staff.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

The Data Privacy & Cybersecurity Associate will provide legal advice on data protection laws, compliance issues, contract drafting, regulatory inquiries, and aid in corporate transactions related to privacy and security.

The AI Systems and Operations Specialist drives AI automation and decision support across HR systems, focusing on operational efficiency and technology implementation.

Design, build, test, and deploy AI agents, agentic workflows, and automation solutions. Partner with stakeholders to prototype and productionize full-stack AI applications across cloud, VPS, and local GPU environments while ensuring scalability, security, and performance.

Deploy, maintain, and optimize AI-powered applications and automation workflows. Build and support full-stack applications integrated with AI platforms, configure and troubleshoot AI solutions, collaborate with business and IT teams to identify automation opportunities, maintain documentation, and provide technical support while staying current with AI tools and best practices.

Lead and execute company-wide AI and analytics strategy while spending ~50% hands-on building data pipelines, SQL queries, ML models, and production ML pipelines. Architect scalable data platforms, enable self-service analytics, measure business and operational metrics, mentor data teams, improve data quality and governance, and integrate predictive and generative AI into operations and customer workflows.

Lead and manage an AI-focused government program from capture through contract execution, ensuring delivery on time and within budget, building customer relationships, coordinating capture and proposal efforts, managing subcontractors and risks, supporting secure operations, and guiding software product development and programmatic reviews.

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.