Top AI & Machine Learning Jobs

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.

Develops data integration workflows, dashboards, statistical and predictive models for biotherapeutics process development. Analyzes complex scientific and process data, identifies insights and anomalies, troubleshoots data and analysis issues, and communicates findings to technical teams. The role also supports project deliverables, external collaborations, process improvements, and training junior staff on data tools, analysis methods, and visualization practices.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Lead end-to-end data science and machine learning initiatives, from identifying business use cases and querying data through prototyping, evaluation, deployment, and monitoring. Partner with Product, Engineering, and Machine Learning Engineering teams across areas such as risk, insurance, and payments. Develop predictive models, statistical methods, deep learning, and LLM solutions while contributing reusable data assets, tooling, MLOps strategy, technical reviews, and data-informed business decisions.

Designs, builds, deploys, and supports production Agentic AI and Generative AI solutions. Develops full-stack applications, APIs, and microservices using AWS and cloud-native architectures. Implements CI/CD, testing, observability, monitoring, security, and production support practices. Collaborates with product and business teams, contributes to architecture decisions, troubleshoots complex issues, establishes engineering standards, and mentors other engineers.

Summer 2027 AI intern supporting responsible AI adoption across Cencora. Responsibilities include creating documentation and enablement materials, conducting research, supporting communities of practice, measuring adoption, assisting AI-related integration projects, analyzing reports, improving processes, collaborating cross-functionally, and presenting project outcomes. The role also develops skills in mentorship, change management, corporate communications, and technology enablement.

Commercial Data Scientist will develop machine learning models, analyze large healthcare datasets, identify clinician behavioral patterns, and create reusable data products. The role partners with Product, Data, Strategy, Insights, and Sales teams to deliver client-facing analyses and data-driven recommendations. Responsibilities include statistical analysis, production model development, complex SQL querying, scalable data processing, automation, and communicating technical insights through compelling visualizations and narratives.

Conduct fundamental research on on-device LLM efficiency and optimization. Develop proposals, survey academic literature, formulate hypotheses, and run experiments involving quantization, fine-tuning, compression, decoding, and model architectures. Collaborate with internal teams and academic labs, transition prototypes into production, publish at leading AI conferences, and independently drive research initiatives from ideation through implementation.

Designs, builds, and maintains AI-powered developer tools and applications across frontend, backend, APIs, cloud infrastructure, LLM features, agentic workflows, and MCP-based services. The role establishes technical vision, architects scalable systems, reviews code and technical plans, improves platform reliability and AI capabilities, supports production integrations, and collaborates with product, design, engineering, and game development teams.