Top AI & Machine Learning Jobs

Leads AI enablement and solution delivery across Finance, including identifying use cases, training employees, designing AI-enabled workflows, integrating ERP and finance systems, and managing data readiness, governance, controls, and audit compliance. Builds solutions for FP&A, close, revenue, audit, and procurement while measuring efficiency, automation, forecast accuracy, and reduced manual effort. Partners with Technology, Data, and enterprise AI stakeholders to align strategy and implementation.

Analyzes structured and unstructured financial and ERP data using Python, SAS, statistical methods, predictive modeling, and advanced algorithms. Supports Oracle E-Business Suite financial processes, data conversion, reporting, and continuity of operations planning. Develops models and actionable insights for business decisions, collaborates with engineering and disaster recovery teams, maintains contingency plans, and executes recovery exercises. Requires a master’s degree, 5+ years of experience, Oracle expertise, certification eligibility, and approximately 25% annual travel to Washington, DC.

Leads enterprise artificial intelligence and digital transformation strategy across manufacturing and corporate functions. Develops AI roadmaps, governance, adoption programs, and business cases; identifies and implements solutions involving predictive maintenance, computer vision, quality inspection, production planning, supply chain, and automation. Oversees pilots, vendors, deployments, risk management, responsible AI, change management, executive reporting, and measurable business outcomes while coordinating cross-functional teams and stakeholders.

Leads Tala’s Machine Learning Engineering team in building and operating platforms for model development, real-time inference, streaming features, batch processing, deployment, monitoring, and production reliability. Manages and develops 4–6 engineers, owns delivery and prioritization, provides architectural leadership, improves engineering practices and SLOs, and partners with Data Science, Data Engineering, Product, Credit, and Business Development teams.

Identify business processes suitable for AI or automation, build and deploy practical solutions, measure their impact, and continuously improve them. Manage AI platform operations, access, licenses, governance, documentation, training, and employee enablement. Prioritize incoming AI requests, ensure security and compliance, and collaborate with teams across the organization to deliver measurable productivity, quality, and cost improvements.

Leads product analytics through advanced analysis, experimentation, KPI development, dashboards, and data storytelling. Designs A/B tests and causal analyses to optimize product performance and user experience. Owns measurement for Xometry’s embedded DFM AI and IQE integration across CAD/PLM partner surfaces and the marketplace, addressing attribution, identity resolution, and low-volume enterprise cohort challenges. Collaborates with product, engineering, design, machine learning, and partner teams to deliver actionable insights and improve experimentation methodologies.

Design and deploy AI-enabled decision workflows for Department of Defense and federal customers. Translate operational challenges into data models, dashboards, AI agents, automated workflows, and predictive or prescriptive solutions. Engage stakeholders, analyze and troubleshoot data, advise end users, support adoption, and collaborate with developers to improve the platform. The role combines solution engineering, analytics, customer advisory, data integration, and AI workflow design.

Own enterprise AI enablement across IRIS, including platform deployment, configuration, governance, agent and connector standards, security, data protection, FinOps, adoption, and change management. Lead the AI Champions network, educate employees, assess emerging AI capabilities, coordinate with IT and business stakeholders, and report on usage, costs, risks, engagement, and business value.

Lead and scale a research team from 5 to 20 researchers developing frontier machine learning systems for forecasting, optimization, and control. The role requires strong ML foundations, hands-on experience training and deploying production models, and first- or second-line management experience. Preferred qualifications include LLM infrastructure expertise, quantitative modeling experience, and startup experience. This is an in-person role in San Francisco, five days per week.

Develop and lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.