Top AI & Machine Learning Jobs

Own a portfolio of organizations and scientific projects advancing AI resilience. Responsibilities include mapping AI-related bottlenecks, developing investment theses, sourcing and diligencing projects, recruiting founding teams, coaching founders, supporting fundraising and operations, serving on boards, and cultivating relationships with philanthropic and public funders. The role requires technical fluency in frontier AI, startup-building experience, fundraising, hiring, budgeting, roadmap development, governance familiarity, and a strong field network.

Develop AI/ML software for signal processing within a research-focused engineering team. The role requires independent work and collaboration on small teams, with professional C++ development experience and expertise applying AI/ML to signal-processing problems. This is a permanent, full-time, on-site position supporting a rapidly growing research department.

Leads enterprise AI governance, portfolio strategy, intake, prioritization, roadmaps, and lifecycle management across UL organizations. Partners with business, technology, legal, security, risk, compliance, finance, HR, vendors, and external partners to operationalize responsible AI, evaluate delivery approaches, measure value, manage adoption, and drive AI literacy. Establishes operating frameworks, governance processes, performance metrics, change programs, and sustainable AI implementation strategies.

AI Business Transformation Interns will evaluate and prototype Generative and Agentic AI use cases using approved tools to improve and automate business operations. They will collaborate with Information Technology, Finance, Supply Chain, and vendors while creating AI-enabled project artifacts, knowledge repositories, and searchable documentation for enterprise software digitization projects.

Leads the bank’s enterprise AI strategy, portfolio, engineering capability, governance, vendor and platform strategy, and workforce adoption. Translates business priorities into production AI initiatives, including generative AI and agentic systems, while ensuring risk controls, security, privacy, auditability, model governance, and measurable value realization. Builds and manages senior AI talent, partners with business and control functions, oversees responsible AI practices, and reports progress, cost, risk, adoption, and outcomes to executive leadership and the Board.

Own and improve Arlo’s healthcare underwriting model using large-scale claims data. Develop evaluation frameworks, engineer predictive features, test machine learning architectures, and validate model improvements through backtesting focused on medical loss ratio and pricing competitiveness. Monitor predictions and policy outcomes, identify bias and risk-estimation gaps, and partner with actuaries and ML engineers to deploy reliable models at scale.

Build and operate production MLOps infrastructure spanning experimentation, model training, deployment, serving, monitoring, retraining, and rollback. Develop ML data and feature pipelines using multimodal manufacturing data, maintain lakehouse and feature-store capabilities, and establish observability and human-in-the-loop feedback systems. Collaborate with data, AI, software, process, materials, and simulation engineers to deliver reliable, scalable ML platforms and model feedback loops.

Analyze big data using data mining, machine learning, and sentiment analysis techniques. Develop and apply data architecture solutions using Java open-source technologies and OpenNLP. The role requires expertise in big data analytics and at least six months of engagement.

Architect and build foundational GenAI platform components, including model-serving infrastructure, distributed systems, cluster orchestration, data pipelines, APIs, embeddings, retrieval systems, and evaluation workflows. Improve deployment pipelines, observability, security, scalability, and developer experience while bringing emerging GenAI techniques into reliable enterprise production environments.

Develop predictive models and data science solutions for automated credit-risk and lending decisions. Responsibilities include collecting, cleaning, visualizing, and analyzing large datasets; identifying patterns; presenting insights to stakeholders; translating models into business strategies; partnering with IT on implementation; and monitoring model performance.