Top AI & Machine Learning Jobs

Designs and maintains the College’s enterprise data and AI platform. Develops integrations, governed data pipelines, warehouses, APIs, MCP services, dashboards, analytical applications, and AI-supported workflows. Establishes testing, monitoring, security, access controls, data governance, and evaluation standards. Partners with institutional offices to translate business needs into secure technical solutions while investigating unfamiliar systems, APIs, schemas, and workflows.

Build production AI systems combining voice interfaces, browser agents, computer vision, and large language models. Responsibilities include developing speech-to-text and text-to-speech systems, browser automation, prompt and LLM workflows, multimodal pipelines, evaluation frameworks, real-time infrastructure, observability, and production performance improvements.

Supports equity research analysts by querying, transforming, analyzing, and combining large datasets; developing statistical models; building AI agents, MCP servers, and data workflows; and ensuring data quality, lineage, governance, and AI accessibility. The role translates complex technical methods into actionable insights, collaborates with research teams and vendors, and contributes to generative AI tooling and platform enhancements.

Develops predictive models and transforms structured and unstructured data into actionable insights. Responsibilities include data wrangling, statistical analysis, machine learning, visualization, reporting, data pipeline management, cloud and DevSecOps integration, root-cause analysis, and technical solution development. The role supports government stakeholders, guides junior personnel, leads data projects independently, validates AI models, documents algorithms, and communicates findings through briefings and presentations.

Research and develop omni-modal multimodal large models, including training data, foundational algorithms, pre-training, supervised fine-tuning, reinforcement learning, evaluation, and downstream applications. Analyze performance bottlenecks, develop first-principles solutions, explore next-generation architectures, and advance multimodal understanding and generation. The role also involves optimizing deep learning operators, model training and inference, CPU/GPU acceleration, and distributed systems.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Leads end-to-end applied data science and AI workstreams for predictive underwriting and pricing, including problem framing, feature engineering, model development, validation, deployment, monitoring, and business-impact measurement. Builds explainable, governed, and reusable production models using healthcare and insurance data. Partners with underwriting, business, product, engineering, and legal teams; mentors others; and advances responsible AI, MLOps, model governance, and shared data science practices.

Design, build, and deploy production-grade agentic AI systems for regulated banking use cases. Responsibilities include multi-agent orchestration, language-model fine-tuning, evaluation frameworks, MCP and A2A integrations, enterprise API development, cloud deployment, observability, and rapid prototyping. The role requires independently delivering tested, production-ready systems while collaborating with researchers, data scientists, and business stakeholders.

Develop audience segments, customer profiles, and predictive models using behavioral, transactional, viewing, and digital data. Apply statistical and machine learning methods for clustering, churn, propensity, content affinity, and customer value analysis. Conduct exploratory analysis, evaluate models, support campaign measurement, and translate findings into business recommendations. Collaborate with Marketing, Product, Media Sales, and Content teams while presenting insights to technical and non-technical stakeholders.

Own end-to-end data science initiatives across growth, personalization, pricing, market execution, risk, financial integrity, and financial crime. Build causal inference, predictive, ranking, and fraud detection models; deploy and monitor them in production; communicate uncertainty and insights to senior stakeholders; and mentor teammates on statistical and modeling best practices.