Top AI & Machine Learning Jobs

Develop and productionize data and AI solutions across the full machine learning lifecycle, including problem identification, model design, training, deployment, monitoring, and impact measurement. Partner with Operations, Engineering, Product, Fraud, Compliance, and Credit teams to manage risk and improve customer experience. Apply statistical techniques, build APIs, integrate ML systems into production services, and collaborate with technical and non-technical stakeholders.

Build AI-powered finance applications using Databricks, Python, LLMs, and governed financial data. Develop tools for variance commentary, financial close checklists, reconciliation, FP&A, natural-language data exploration, and financial analysis. Partner with finance stakeholders, measure adoption and productivity gains, and maintain governance separating AI recommendations from financial controls. The role requires software engineering, finance process knowledge, and experience with Databricks, LLM applications, finance systems, and production-ready applications.

Leads AI strategy and ecosystem solutions for Fortune 500 clients, designing agentic workflows, multi-agent architectures, and AI-native storage solutions. Partners with executives and internal Product, Engineering, and GTM teams to translate business challenges into scalable technical blueprints. Evaluates LLM, RAG, and AI agent trends, develops ROI models and proof-of-concepts, and shapes enterprise platform strategy while building repeatable solutions and team capabilities.

Leads hands-on development of applied AI and software engineering solutions across architecture, coding, testing, deployment, and support. Designs GenAI applications, integrates LLMs, builds RAG pipelines, and applies agentic AI practices throughout the software lifecycle. Maintains technical standards, reviews code, mentors engineers, collaborates with product and customer teams, and delivers scalable cloud-native solutions using rapid experimentation and automated quality practices.

Conduct quantitative labor market research for clients by querying, cleaning, and analyzing large datasets with SQL, Python, and Snowflake. Build data pipelines, indices, repeatable workflows, dashboards, and client-ready materials. Apply statistical methods and labor economics concepts, present findings, use LLM tools responsibly, document methodologies, and collaborate on research, product work, proposals, and client deliverables.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Extract structured facts from long, unstructured and semi-structured documents while preserving exact source citations. Validate evidence, distinguish stated totals from derived calculations, handle ambiguity without guessing, and apply established extraction methods consistently. Collaborate with entity resolution and retrieval teams to support downstream systems. The role requires strong Python and NLP or information-extraction experience, U.S. citizenship, and an active Secret clearance.

Build data preprocessing and generation pipelines, run model training and fine-tuning workflows, design benchmarking and evaluation suites, and operationalize research models into reproducible production workflows. The role collaborates with researchers, communicates metrics and results, and owns measurable machine learning outcomes from data preparation through delivery.

Design and implement machine learning, statistical, and decision-analysis models in cloud-native environments. Build large-scale Spark data pipelines, perform data cleaning and trend analysis, and translate organizational objectives into practical ML solutions. Ensure model documentation and bias testing comply with federal AI governance requirements. Collaborate with policy, technical, and program teams, contribute to open-source solutions, and manage code through GitHub.

Leads enterprise AI adoption by establishing governance, standards, intake, portfolio management, security controls, evaluation practices, and enablement patterns. Partners with Legal, Privacy, Security, Technology, FinOps, and business teams to identify automation opportunities, manage AI platforms and use cases, monitor value and risk, and support scalable solution delivery. Builds and develops a geographically distributed AI Enablement team while evaluating emerging technologies and improving enterprise AI capabilities.