Top AI & Machine Learning Jobs

Build production-quality AI applications using Python and existing large language models. Design multi-agent and multi-step workflows with prompt chaining, tool use, and orchestration; engineer and evaluate prompts; integrate AI into RESTful APIs and microservices; and deploy solutions on AWS. Preferred work includes RAG pipelines, vector databases, observability, exploratory data analysis, and MLOps/LLMOps practices.

Develop and maintain the U.S. Census Bureau’s LLM Autocoder using Python, LLMs, prompt engineering, text classification, structured outputs, and testing. Build survey-specific pilots, troubleshoot production issues, evaluate model changes, and collaborate with data scientists and MLOps engineers on AWS deployment, rollback, and verification. Maintain documentation and support government handoff while meeting Census security and operational requirements.

Data Science Interns analyze and clean large datasets, create reports, dashboards, and visualizations, and support predictive modeling, statistical analysis, and machine learning initiatives. They identify trends, validate data quality, collaborate with business partners, and present insights and recommendations. The role offers hands-on experience with business data, mentoring, and analytics projects while requiring strong analytical, communication, and organizational skills.

Build and maintain data science models for a PRO loyalty program, including churn, retention, customer lifetime value, propensity, segmentation, reward redemption, and program economics. Design experiments, measure incrementality, identify at-risk members, and support loyalty and marketing decisions. Partner with Finance, Marketing, PRO, IT, and Data Engineering to translate analytical findings into actionable strategies, present results to stakeholders, and maintain reproducible model documentation.

Develop and deploy data analytics, machine learning, and AI applications. Responsibilities include analyzing complex datasets, building models, deploying containerized applications to Kubernetes, integrating LLM-powered features, monitoring and troubleshooting AI systems, and creating Streamlit prototypes. The intern will collaborate with data science teams and stay current with AI, data science, and cloud-computing practices.

Leads AI engineering strategy and a high-performing team developing scalable, secure, and responsible AI solutions. Drives LLM-powered decision automation, fraud detection, process optimization, model evaluation, monitoring, governance, A/B testing, backtesting, and drift detection. Partners with product, UX, and engineering stakeholders to translate business priorities into AI roadmaps and production capabilities. The role requires extensive software, machine learning, or data engineering experience and strong technical leadership.

Leads the News + AI Innovation Lab at Arizona State University, establishing its scope, partnerships, and public presence. Owns AI product vision, prioritization, road mapping, iteration, testing, user feedback, implementation, and partner onboarding. Develops responsible technology initiatives at the intersection of journalism, research, and technology while collaborating with faculty, fellows, and cross-functional staff.

Build and operate secure data pipelines and workflows supporting machine learning, NLP, retrieval, RAG, and advanced analytics. Prepare structured and unstructured data, develop features and embeddings, enforce data quality and lineage, troubleshoot integrations, document architectures, and support deployment in Federal on-premises, cloud, or hybrid environments. The role requires Python and SQL expertise, cross-functional collaboration, and compliance with Federal security and governance requirements.

Build autonomous AI agents for pharmaceutical workflows, including orchestration, communication systems, integrations with pharmacy systems and APIs, evaluation, observability, and real-time processing infrastructure. The role requires architectural ownership, backend development, cloud deployment, distributed systems expertise, and end-to-end product ownership in a hybrid Manhattan environment.

Senior Data Scientist partnering with Product, Design, Engineering, Finance, and Marketing to drive user growth through data science, causal inference, experimentation, and analytics. Responsibilities include developing LTV and retention models, designing A/B tests, creating measurement frameworks and dashboards, translating complex analyses into recommendations, and strengthening data foundations for scalable experimentation.