Top Artificial Intelligence Jobs

Summer intern supporting integrated supply chain and manufacturing teams in manufacturing engineering, operations, quality and continuous improvement, or supply chain planning. Responsibilities may include process improvement, technical solution deployment, quality and productivity initiatives, strategic sourcing, logistics, transportation, and production planning. This is a 10–12 week, 40-hour-per-week, on-site assignment requiring relocation to a Wisconsin or Ohio location.

Works with IT professionals in an agile environment on software design, development, testing, prototyping, debugging, technology investigations, and project management. Potential focus areas include software development, AI/ML, cybersecurity, networking, infrastructure, cloud engineering, data engineering, and analytics. Interns receive mentorship and collaborate on digital transformation initiatives.

Own the strategy, roadmap, reliability, governance, security, privacy, compliance, and lifecycle management of an enterprise AI platform supporting search, assistants, agents, and workflow orchestration. Manage backlogs, define requirements and service metrics, oversee operational readiness, coordinate engineering and business stakeholders, and drive continuous improvement. Enable platform adoption while promoting responsible AI, resilience, user trust, and effective governance across a complex global enterprise.

Own the company’s early revenue engine, including account mapping, researched outbound, warm-channel development, conferences, pilots, procurement, proposals, pipeline reporting, and GTM systems. This founding commercial hire will build repeatable sales processes, improve deal velocity, manage customer and market feedback, and work closely with the founders on enterprise AI sales. The role requires strong judgment, executive presence, persuasive writing, and demonstrated ownership of measurable outcomes.

Leads Innovative Medicine Supply Chain’s decision science strategy, roadmap, governance, technical innovation, and capability development. Oversees a team of more than 10 managers and data scientists delivering optimization, simulation, forecasting, AI, machine learning, and advanced analytics solutions. Partners with business and technology leaders to build scalable digital products, establish model standards, develop talent, and connect technology investments to supply chain outcomes in a regulated pharmaceutical environment.

Conduct foundation-model research for robotic manipulation and high-precision manufacturing. Responsibilities include data curation, simulation, model training, evaluation, rigorous testing on industrial robots, and deployment to production hardware. The intern will work independently on ambiguous AI problems, develop multimodal models, use reinforcement or imitation learning, collaborate cross-functionally, and write maintainable production-quality R&D code.

Provide software technical support through phone, email, and chat; diagnose application and system issues; collaborate with development teams; document problems and create knowledge-base content; train customers; participate in on-call rotations; and travel approximately 5% to customer and company locations. The role requires database and Windows administration experience, strong communication and troubleshooting skills, and openness to AI technologies.

Design, develop, and operate a production agentic data platform using Spark, Kafka, Airflow, Snowflake and AWS. Write secure high-quality code across frontend and backend, leverage enterprise AI-assisted development and LLMs (Claude Code, agents, RAG, embeddings), build autonomous agents and MCP server components, apply secure/responsible AI practices, troubleshoot systems, and own full-stack application/platform delivery with CI/CD, containers, and observability.

Lead an agile engineering team to design, build, and operate high-scale, secure Java applications and internal developer platforms. Drive adoption of enterprise-authorized AI-assisted development tools, improve observability and reliability (SLOs, alerting), own on-call and incident response, and mentor peers on responsible AI usage and best engineering practices.

Leads complex, cross-functional Enterprise Technology, AI, and Data programs from planning through delivery. Responsibilities include roadmap development, dependency and resource management, risk and issue tracking, governance, executive communications, operational transformation, enterprise application initiatives, and AI workflow adoption. The role aligns technical and business stakeholders across Finance, Legal, Sales, Marketing, Product, and other functions while improving program delivery practices in a remote, globally distributed environment.

Manages end-to-end songwriting camps and studio activations, including technical setup, logistics, creative facilitation, live Suno demonstrations, artist education, and troubleshooting. Partners with product managers and engineers to beta test features and translate artist feedback into product insights. Builds relationships across the music industry, supports activation strategy, and travels frequently to domestic and international camps, studios, events, and meetings.

Leads research and architectural evaluations of emerging technologies, including AI, quantum computing, edge architectures, and spatial computing. Develops white papers, technical assessments, strategic roadmaps, executive briefings, and rapid-response analyses for federal modernization initiatives. Establishes AI governance practices, aligns technology recommendations with mission objectives, and ensures compliance with federal standards and security protocols. Translates complex technical concepts into actionable recommendations for senior leadership and supports infrastructure modernization addressing border security challenges.

Conduct AI strategy assessments across private equity portfolio companies using a structured methodology. Build feature inventories, workflow maps, roadmap critiques, opportunity rankings, comparative scorecards, and prioritized recommendations tied to commercial outcomes. Analyze customer research, willingness-to-pay studies, market trends, feasibility, economics, and disruption exposure. Produce executive-ready written deliverables, defend recommendations, and manage multiple concurrent assessments under tight deadlines.

Build strategic relationships with frontier AI labs, model companies, and data ecosystem stakeholders. Identify opportunities, map organizations and research priorities, connect prospects with internal commercial leaders, and support opportunity progression. Own inbound-demand initiatives including events, conferences, ecosystem partnerships, content, and executive programs. Track relationships and market developments while communicating credibly with researchers, founders, executives, and commercial stakeholders.

Lead design and deliver multi-cloud AI/ML solutions that ingest and correlate telemetry (metrics, logs, traces), build predictive anomaly detection and forecasting models, automate closed-loop remediation, integrate with ticketing systems, and provide self-service or conversational interfaces to reduce MTTR and ticket volume.

Design, build, operate, and scale enterprise AI platforms across public and private clouds. Develop MLOps capabilities, CI/CD pipelines, infrastructure automation, observability, monitoring, governance, and AI deployment services. Support production machine learning and generative AI workloads, troubleshoot incidents, improve reliability and performance, collaborate with engineering, security, and governance teams, evaluate emerging technologies, and mentor peers.

Own the strategy, configuration, and continuous improvement of HoneyBook’s support chatbot and related Help Center and internal documentation. Analyze resolution data, identify content gaps, prioritize roadmap investments, configure Intercom, maintain data connectors, evaluate AI tools, and build internal low-code applications. Create clear technical documentation, improve AI-assisted workflows, maintain governance, and collaborate with Member Care, Product, QA, Marketing, and other teams to improve member and support specialist experiences.

Lead cross-functional cybersecurity programs to reduce enterprise risk from frontier AI models. Drive planning through execution, prioritize vulnerabilities, expand WAF coverage, enable AI-driven testing and automated patching, strengthen identity controls and telemetry, escalate risks, provide executive reporting, and coordinate remediation across technology, product, operations, and business stakeholders.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Design, build, operate, and scale enterprise AI platforms across public and private clouds. Develop MLOps, infrastructure automation, CI/CD, observability, governance, deployment, monitoring, and lifecycle-management capabilities for machine learning and generative AI workloads. Support production platforms, troubleshoot incidents, improve reliability and performance, collaborate with engineering, security, and governance teams, evaluate emerging technologies, and mentor peers.

Design, implement, and deliver AI-enabled product features (LLMs, agents, ML). Build cloud-native services, ensure observability, reliability and scalability, troubleshoot production issues, participate in architecture reviews, and collaborate with product, data, and engineering teams.

The Staff Forward Deployed Engineer at GitLab works with strategic accounts to adopt GitLab and GitLab Duo Agent Platform, guiding technical discovery and building reusable solutions for complex enterprise environments.

Design, build, operate, and scale enterprise AI platforms across public and private clouds. Develop MLOps, automation, CI/CD, infrastructure, observability, monitoring, governance, and deployment capabilities for machine learning and generative AI workloads. Support production systems, incident response, troubleshooting, reliability, and lifecycle management. Collaborate with engineering, infrastructure, security, and governance teams, evaluate emerging technologies, contribute to architecture and strategy, and mentor peers.

Design, develop, and deploy production AI/ML solutions for underwriting and healthcare benefits modernization. Build RAG pipelines, integrate LLMs, support agentic AI using MCP and agent-to-agent protocols, and create secure, high-performance APIs for machine learning capabilities. Collaborate with software, data engineering, and underwriting teams to deliver scalable microservices and pipelines while ensuring model availability, security, monitoring, testing, and performance.

Design, build, and scale a multi-tenant AI Ops platform: develop high-throughput microservices, GraphQL/REST APIs, backend infrastructure, data systems, telemetry, and CI/CD deployments for operationalized ML/AI services.