Top AI & Machine Learning Jobs

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and operate uplift and machine learning models that optimize personalized retention bonuses. Responsibilities include feature pipeline development, model deployment, monitoring, experimentation, causal analysis, large-scale behavioral data analysis, and communicating business impact to Marketing stakeholders. The role also involves weekly model operations, cross-functional collaboration, documentation, code review, and developing reusable modeling practices.

Leads Adyen’s Credit Risk Modeling team, overseeing the design, productionization, deployment, monitoring, and improvement of machine learning models and rule-based decision systems for credit underwriting. The manager provides hands-on technical leadership, mentors machine learning scientists and data engineers, shapes architecture and product roadmaps, collaborates cross-functionally, recruits team members, and builds an inclusive, high-performing engineering culture.

Develop and deploy reliable machine learning, LLM-powered agent, and agentic workflow systems for government and enterprise customers. Integrate models with tools, APIs, MCP servers, and data sources; build evaluation frameworks for performance, reliability, and safety; improve Chariot, the company’s AI operations platform; and support mission-critical deployments. Collaborate with data scientists, software engineers, DevOps teams, and customers in secure government environments.

Design and scale data pipelines and ML/LLM systems, build agentic automation for pipeline generation and maintenance, improve data monitoring, and collaborate with analysts, product, and ML teams to deliver reliable end-to-end data and AI infrastructure for a high-growth e-commerce platform.

Develop and ship a meaningful engineering project, monitor its production deployment, collaborate with a mentor and manager, test and document work, and present results to the engineering organization. The role may involve machine learning, backend or frontend development, APIs, data processing, deployment, and testing using technologies such as Python, Java, JavaScript, AWS, AngularJS, or React.

Own and build the company’s machine learning infrastructure, including a shared feature store, model-serving systems, drift monitoring, and unified deployment pipelines. Establish long-term MLOps standards and processes from the ground up, while collaborating with data scientists and engineers to drive adoption and improve production model reliability.

Build and operate production-grade AI developer tooling, agentic workflows, and shared infrastructure that improve software development across engineering teams. Responsibilities include AI-assisted code review, testing, issue triage, model integrations, evaluations, monitoring, cost controls, adoption enablement, and cross-functional collaboration with security, platform, quality, product, and design teams.

Manage AI and machine learning products for materials science, including customer demonstrations, roadmap planning, product expansion, ML model development, and leadership of the materials science ML team. The role requires deep expertise in materials science and ML, software-industry experience, customer engagement, public presentations, and global travel.

Build and operate production AI systems for FDA regulatory intelligence, including evaluation pipelines, document ingestion and retrieval, multi-agent workflows, data pipelines, cloud infrastructure, and secure multi-tenant services. Collaborate with former FDA experts to translate domain judgment into product requirements and evaluation cases. Own architecture, reliability, observability, cost, security, and rapid 0-to-1 delivery across the stack.