Top Artificial Intelligence Jobs

Lead design, build, and productionalize generative AI applications and platform capabilities. Automate ML pipelines, optimize models (LoRA/QLoRA/PEFT), implement guardrails and monitoring, support RAG and vector DB tuning, mentor engineers, and collaborate with product and data teams to deploy safe, reliable GenAI features across web and mobile.

No responsibilities provided in the job posting.

Design, develop, and deploy end-to-end AI/ML applications (RAG, chatbots, agentic workflows). Build scalable data pipelines, apply graph analytics, entity resolution, and NLP for risk scoring, anomaly detection, and investigative prioritization. Develop models, REST APIs, and deploy/manage solutions in AWS to support federal mission use cases.

The Senior Manager of AI and Innovation Communications will execute communications strategies for AI product launches, customer storytelling, and executive content while building media relationships and supporting partnership announcements.

Lead architecture and development of a scalable, real-time Platform Chat product using LLMs and agent frameworks. Build orchestration layers, workflow automation, LLM integrations, and systems for observability, safety, and performance while mentoring engineers and collaborating with product and data science teams.

Provide technical leadership for physical AI, virtual commissioning, and digital twin robotics applications. Support pre- and post-sales, perform feasibility studies, simulations, troubleshooting, root-cause analysis, and proof-of-concepts. Develop applications, improve robotics software, support vision/iRVision, and collaborate with sales and engineering teams to integrate and optimize FANUC robot solutions.

Design, deploy, and manage AI-enabled legal tools and digital employees; support AI governance, data analytics and reporting for contracts; administer CLM and legal tech platforms; maintain processes, controls, and playbooks; and coordinate with attorneys, business teams, and external counsel to improve contracting, vendor management, and data quality.

Lead end-to-end product ownership for a suite of GTN AI/analytics products (Clarity, Compass, Dynamic Forecasting). Define vision, roadmap, and KPIs; prioritize use cases; drive delivery with cross-functional pods; manage vendors and platform selection; standardize and scale GTN workflows; ensure responsible AI, governance, and measurable business impact.

Lead end-to-end technical programs to integrate AI (LLMs, agents, chatbots) into chip design workflows. Plan, coordinate, and drive cross-functional execution across ASIC, platform, and third-party partners, measure program metrics, manage risks, and translate user requirements into scalable production-ready AI capabilities for silicon development.

Design, build, and deploy LLM-powered tools, RAG-based knowledge systems, and agentic workflows to automate SOC integration tasks. Own reliability, evaluation, and observability for AI systems and collaborate with SOCD teams to scope, validate, and measure productivity gains from deployed automation.

Design and implement AI solutions, focusing on data pipelines, workflow automation, and ensuring security and governance in a production environment. Collaborate to create reliable, scalable models and optimize enterprise data integration.

Design, implement, and train novel deep learning models at scale. Prototype architectures for tabular, relational, and graph data. Apply cutting-edge AI research, advise strategy, and partner with engineering, product, and design to productionize research into customer-facing features.

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 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.

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 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.

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 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.