Top AI & Machine Learning Jobs

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Lead the delivery of ML initiatives, define roadmaps with cross-functional teams, and develop ML models for conversational agents and smart tools, enhancing customer support.

As a Senior Machine Learning Engineer in Model Risk Management, you will evaluate and validate models that assess risk in lending and fraud, ensuring their robustness and reliability. You will collaborate with model owners, tackle model challenges, and build evaluation tools critical for understanding model performance.

Lead end-to-end data science for lending products: define measurement frameworks, analyze experiments and cohorts, evaluate risk and model performance, use AI tools to accelerate analysis, build reusable tooling, and mentor junior scientists while partnering with product, engineering, and risk stakeholders.

Lead end-to-end data science for lending products: define measurement frameworks, analyze customer and risk data, design experiments, monitor model and policy performance, and build reusable tooling. Use AI tools and agent workflows to accelerate analysis, mentor junior scientists, set technical standards, and partner with product, engineering, and risk to drive product and business decisions.

Lead end-to-end data science for lending products: define metrics, run experiments, build measurement systems, evaluate model and policy performance, use AI tools to accelerate analysis, and mentor the team while partnering with product, engineering, and risk stakeholders to drive credit product decisions.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

Graduate research internship to own end-to-end research on Customer World Models and proactive intelligence: design representations, develop methods (foundation models, RL, causal reasoning, agentic systems), run experiments, publish, and help ship ML systems that anticipate and act on customer needs.