Top Artificial Intelligence Jobs

Analyze wafer-fabrication, equipment, sensor, metrology, and manufacturing data using statistical analysis, AI, and machine learning. Develop anomaly detection, yield prediction, root-cause, data pipeline, dashboard, and model-monitoring solutions. Integrate diverse manufacturing data sources, support experiments and process improvement, validate and deploy models, and communicate actionable engineering insights to technical and management stakeholders.

Own the quality strategy and release gate for Unity’s Gaming AI organization. Build AI-native testing, evaluation, defect intelligence, test automation, and quality measurement systems across frontend, backend, and dependent systems. Establish methods for evaluating nondeterministic AI outputs, including correctness, latency, cost, and consistency. Lead through influence across engineering, art, and product while connecting quality investments to creator and player outcomes and making evidence-based release decisions under schedule pressure.

Develops AI solutions, automation pipelines, reports, and dashboards using service operations data. Audits and validates AI-generated outputs, monitors model reliability, bias, and accuracy, and translates business challenges into measurable workflows and KPIs. Creates Tableau and Power BI visualizations, presents insights to executives, trains stakeholders, and partners with engineering teams to deploy workflow automation. Requires accurate analytics, reporting, and data management for field service operations.

Supports an AI operations platform by documenting business workflows, testing and QAing AI protocols, triaging bugs, prototyping tools and integrations, maintaining documentation, conducting research, and completing frontend, backend, and API development tasks. The intern collaborates with engineers and business teams while building production solutions using programming languages, databases, APIs, and AI/LLM platforms.

Supports AI-enabled tools for competitive pricing automation and online product placement tracking. Responsibilities include researching data sources, organizing scraped data, monitoring retailer websites, defining tracking requirements, partnering with stakeholders, creating weekly reports, and documenting processes. The internship requires strong problem-solving, communication, attention to detail, computer skills, and eagerness to learn in a consumer products environment.

Supports development of an AI feature validation system, including expert-reviewed test sets, scoring logic, automated test execution, regression monitoring, trend dashboards, release gates, and workflow documentation. The intern will conduct structured interviews with nontechnical associates, translate business workflows into test cases, analyze results, and collaborate cross-functionally while learning company processes.

Apply AI/ML and NLP techniques to analyze, triage, and characterize large language datasets. Develop and train models, create analytical tools, perform data mining and qualitative characterization, evaluate model performance, document methodologies, and collaborate with technical and mission teams to support operational objectives.

Leads the design, development, and deployment of scalable, reliable software and infrastructure solutions. Builds AI agents, automation workflows, observability integrations, and monitoring systems using technologies including C++, Python, Java, Claude, Codex, BigPanda, ITMP, and Prometheus. Collaborates across teams, drives engineering standards, mentors junior engineers, and manages complex end-to-end projects.

Design, develop, maintain, and execute complex financial models for loss forecasting, stress testing, pricing, originations/collections, and portfolio optimization. Use Python or R (and SQL/SAS/other languages) for data processing and model development, build Power BI analytics, collaborate with business partners and regulators, support model validation/audits, research industry/academic methods, and develop data environments to support quantitative activities.

Lead and grow the engineering team responsible for PermitFlow’s agentic AI infrastructure and evaluation systems. Own architecture for orchestration, model serving, reliability, latency, cost, and failure handling. Build evaluation pipelines using golden datasets, LLM-as-judge systems, human annotation, and online monitoring. Partner with product leadership to define automation quality standards, drive the technical roadmap, improve production reliability, and hire and develop AI-focused engineers.

Owns a delivery team's product outcome from discovery through production, combining product management, UX and interaction design, front-end engineering, and delivery leadership. The role directs AI coding agents, writes production React interfaces, defines prompts and evaluation criteria, enforces human review and accessibility standards, and shapes scalable operating practices within a regulated FedRAMP environment.

Owns the roadmap, prioritization, delivery, and optimization of internal AI products supporting Revenue Operations. Partners with RevOps, engineering, data, and product teams to develop AI capabilities for forecasting, sales execution, deal governance, proposals, and enablement. Uses stakeholder research, workflow analysis, usage data, and outcome metrics to improve adoption, accuracy, productivity, process efficiency, deal quality, and revenue impact.

Leads firmwide legal AI initiatives by identifying use cases, building and implementing AI agents, evaluating emerging solutions, and supporting attorney adoption. Collaborates with attorneys, IT, vendors, and leadership to diagnose technical issues, manage pilots, document use cases, deliver training and content, assess risks, and provide recommendations aligned with the firm’s AI strategy.

Leads the strategy, development, deployment, commercialization, and scaling of AI products across Granicus’s government technology platform. Owns AI roadmaps, pricing and packaging, deployment standards, adoption metrics, productization, and forward-deployed engineering. Partners across Product, Engineering, Data Science, Sales, Finance, and Customer Experience to convert customer deployments into repeatable, revenue-generating offerings and ensure measurable outcomes across government agencies.

Supports early-stage AI sales opportunities for public sector customers by conducting discovery, identifying automation needs, qualifying prospects, assisting with demos and pilots, maintaining Salesforce pipeline data, and partnering with sales, marketing, RevOps, and technical teams. The role contributes to demand generation, opportunity progression, customer feedback collection, and solution alignment for Granicus AI offerings.

Leads firmwide legal AI initiatives by identifying use cases, designing and building AI agents, evaluating emerging solutions, and implementing pilots. Partners with attorneys, IT, vendors, and leadership to address technical challenges, drive adoption, provide training, document limitations, and recommend improvements. The role also manages testing milestones, researches AI products, supports client inquiries, and prepares qualitative and quantitative reports.

Develops and deploys AI and machine learning solutions for intelligence and cyber workflows in classified environments. Responsibilities include building LLM-powered workflows and agent-based automation, integrating AI into operational systems, developing data pipelines, evaluating models, optimizing workflows, and conducting operational testing. The role collaborates with mission users, analysts, software developers, and leadership to deliver reliable, scalable, secure, and production-ready AI capabilities.

Develop and deploy enterprise-scale AI and machine learning solutions for secure government and intelligence missions. Build LLM-powered workflows, agent-based automation, data pipelines, and production ML systems. Integrate AI into operational platforms, conduct model evaluation and testing, optimize workflows, and ensure scalability, reliability, security, and governance. Collaborate with mission users, analysts, software developers, and leadership while providing technical guidance and mentoring.

Design and implement AI/ML capabilities for cyber and intelligence workflows, including LLM-powered solutions, agent-based automation, and API or enterprise data integrations. Translate mission-user workflows into automation requirements and executable logic, collaborate with analysts and developers, and ensure reliable production deployment in classified environments. The role requires Python, TS/SCI with polygraph clearance, and qualifying education and experience.

Designs and implements AI/ML capabilities for cyber and intelligence workflows, including LLM-powered systems, agentic automation, API and data-store integrations, and production deployments in classified environments. Works with mission users to document workflows, translate decision points into executable logic, and improve analytical efficiency. Collaborates with operators, analysts, developers, and leadership while ensuring secure, governed, reliable system integration.

Build and lead the organization-wide adoption of generative AI, agentic systems, and AI-assisted development. Design production automation agents, evaluate coding tools, modernize rules-based workflows with ML, and establish responsible AI guardrails. Partner with engineering, product, data, and clinical teams to identify high-impact opportunities, own the enablement roadmap, measure outcomes, and drive adoption through working software, documentation, and hands-on collaboration.

Build, secure, and operate an Azure Government AI platform supporting Azure AI Foundry and approved generative AI models. Configure AI projects, deployments, networking, identities, monitoring, cost controls, and integrations. Automate repeatable infrastructure and release processes, troubleshoot across cloud layers, and create operational documentation. The role requires strong Azure engineering, AI platform, security, networking, monitoring, and scripting experience, with responsibility for reliable developer experiences and governed AI usage.

Develop, optimize, test, and maintain prompts for conversational AI, automation, and business applications. Evaluate AI outputs, conduct comparative experiments, improve response quality, and create reusable prompt libraries. Translate business needs into prompting strategies while collaborating with AI engineers, trainers, product teams, and stakeholders. Document standards, support governance, research emerging techniques, and contribute to Generative AI implementation and continuous improvement.

Develop, deploy, and optimize scalable machine learning solutions, including predictive models, deep learning systems, data pipelines, and model-serving infrastructure. Monitor model performance, manage retraining and lifecycle processes, ensure quality and compliance, and support responsible AI practices. Collaborate with data scientists, engineers, product teams, and business stakeholders while researching emerging AI technologies and improving AI architectures.

Monitors and improves AI-powered systems by tracking performance, utilization, KPIs, incidents, and operational trends. Develops reports and dashboards, identifies efficiency opportunities, supports governance and compliance activities, documents procedures, and collaborates with AI engineers, product teams, business stakeholders, and operations leaders to resolve issues and improve adoption.