Top AI & Machine Learning Jobs

Manage AI and machine learning products for materials science, including customer demonstrations, roadmap planning, product expansion, ML model development, and leadership of the materials science ML team. The role requires deep expertise in materials science and ML, software-industry experience, customer engagement, public presentations, and global travel.

Build and operate production AI systems for FDA regulatory intelligence, including evaluation pipelines, document ingestion and retrieval, multi-agent workflows, data pipelines, cloud infrastructure, and secure multi-tenant services. Collaborate with former FDA experts to translate domain judgment into product requirements and evaluation cases. Own architecture, reliability, observability, cost, security, and rapid 0-to-1 delivery across the stack.

Develop and deploy AI capabilities across enterprise ERP platforms including Workday and SAP Ariba. Build intelligent agents, chatbots, automation, integrations, and custom GenAI/LLM solutions. Establish PaaS environments, cloud services, deployment pipelines, security, governance, and engineering processes. Lead AI projects from concept through production, evaluate vendor versus custom solutions, support technology roadmaps, and promote AI literacy. Provide production support and training while aligning solutions with business needs and measurable ROI.

Build and deploy AI agents, automations, dashboards, and data products for People Operations, compensation, leveling, and recruiting analytics. Diagnose ambiguous business questions, validate confidential people data, support compensation planning, improve HRIS infrastructure, and translate analysis into executive-ready recommendations. Partner with Total Rewards and Talent Acquisition while proactively preparing tools for major HR cycles.

Participate in a paid, project-based summer internship at the Federal Reserve Bank of Chicago. Interns work 40 hours weekly for 10–12 weeks, contribute to departmental assignments, interpret data and analytics, develop insights, support decision-making, present information, and collaborate with internal and external stakeholders in an onsite or hybrid environment.

Leads the company’s AI-first enterprise and digital technology strategy, including AI governance, enterprise modernization, cloud, data platforms, software development, digital commerce, cybersecurity, and automation. Advises the CEO and board, oversees global technology teams and budgets, drives digital transformation and M&A integration, and builds scalable technology capabilities that improve innovation, productivity, consumer experiences, and profitable growth.

Join Celonis’s 12-month Orbit Program as an Associate Value Engineer. Consult customers, identify business challenges, build AI and machine-learning prototypes, develop SQL and PQL data models, lead proof-of-concept projects, present solutions to executives, quantify ROI, and establish trusted customer relationships. The role combines technical analytics, enterprise software consulting, customer engagement, and value realization, with mentorship and training provided.

Lead the architecture and development of secure, scalable GenAI platforms, including LLM applications, agentic workflows, RAG systems, evaluation frameworks, observability, multi-tenant infrastructure, security controls, developer APIs, and deployment tooling. Optimize model performance, cost, latency, and reliability while integrating providers and frameworks such as OpenAI, Bedrock, MCP, and LangGraph. Develop software using C#, Java, JavaScript, Go, or Python.

Own the vision and roadmap for AI-powered investment operations products across post-trade workflows, including reconciliations, settlements, exception management, and data quality. Partner with Operations and Engineering to deliver production-grade machine learning and generative AI solutions, define requirements, manage agile product delivery, validate use cases with clients and users, and drive adoption and measurable business outcomes.

Build and deliver production-grade agentic AI systems for wealth management, using Java and modern software engineering practices. Responsibilities include designing scalable architectures, developing and testing solutions, applying AI-assisted development tools, validating adaptive systems, participating in deployments and code reviews, troubleshooting root causes, and contributing to reusable engineering foundations. The role also requires collaboration in architecture discussions, continuous learning, and understanding business outcomes.