Top AI & Machine Learning Jobs

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.

Build unified structured and unstructured data pipelines, predictive and prescriptive analytics, churn models with quantified uncertainty, and production LLM agents. Own enterprise AI architectures, RAG systems, MLOps and LLMOps standards, evaluation, monitoring, cost optimization, security, and reliability. Partner with business stakeholders to translate ambiguous problems into scalable AI solutions and document reference architectures adopted by other teams.

Build Syntro’s technical foundation across the stack, including systems that structure procurement data from emails, spreadsheets, ERP records, quotes, and supplier information. Design reliable AI agents that reason and act under uncertainty, establish scalable architecture and engineering standards, operate production systems, and work closely with customers. The role offers significant ownership over technical decisions, product direction, infrastructure, observability, and eventually the engineering team.

Design, build, and deploy agentic AI solutions (Google Gemini, Claude, ADK); maintain BigQuery data warehouse and ETL pipelines; troubleshoot tracking across Google Analytics, Omeda, Piano, and Salesforce; create dashboards and monitoring to ensure data quality and measure agentic impact across Sales, Marketing, Editorial, and Finance.

Administers UMM data-management infrastructure, including access controls, configuration, dashboards, data flows, ingestion validation, monitoring, and security compliance. Provides advanced tier-two technical support, troubleshoots issues, supports testing and user acceptance, documents configurations, and coordinates enhancements with engineering and data stakeholders. The role supports distributed DoD and Intelligence Community users and requires on-site work in a DIA SCIF with an active TS/SCI CI Poly clearance.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Leads and manages ML engineers working across computer vision, robotics, agentic workflows, and ML platform infrastructure. Owns technical direction, hiring, team development, roadmap commitments, production deployment, monitoring, incident response, reliability, and cross-functional collaboration with product and software engineering teams. This player-coach role requires hands-on oversight of production ML systems, platform architecture, evaluation, safety, and operational quality.

Leads the DataOps and MLOps platform for Databricks on Azure, establishing CI/CD, orchestration, observability, governance, infrastructure automation, and reliable production deployment of data pipelines and machine-learning models. Owns Dagster, dlt, dbt, MLflow, Unity Catalog, Terraform, security, cost optimization, incident practices, compliance automation, and model monitoring. Provides technical leadership, mentors engineers, and ensures data and ML systems are reliable, auditable, secure, and cost-effective in a regulated insurance environment.

Develop analytical and AI-enabled capabilities for a federal data modernization effort. Translate stakeholder questions into statistical analyses, predictive models, forecasts, and scenario analyses; build governed datasets using Databricks, BigQuery, Python, SQL, and PySpark; validate and document models; maintain analytical products; and communicate visualizations, dashboards, findings, assumptions, and limitations to technical and non-technical audiences.