Top AI & Machine Learning Jobs

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Integrate Workday/Notion, ensure security and data governance, design multi-model LLM reliability, and own production lifecycle including CI/CD, monitoring, and incident response while working with non-technical stakeholders.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Turn messy business requirements into production systems, integrate with Workday/Notion/case tools, ensure data governance and security, design multi-model LLM reliability controls, and operate full lifecycle including CI/CD, monitoring, and incident fixes while partnering with non-technical stakeholders.

Lead and grow a platform engineering team responsible for ML training and serving. Define roadmap, drive execution, ensure reliability, optimize GPU and deployment infrastructure, and collaborate with ML, product, and infra teams to support high-priority ML initiatives.

Lead and grow an ML training & serving platform team, define and execute roadmap for model training, deployment, GPU infrastructure, and low-latency serving, drive operational health, partner with ML and infrastructure teams, and recruit and develop platform engineers.

Lead delivery of ML feature platform initiatives: design, build, and operate backend systems for feature creation, storage, backfilling and serving. Collaborate with product, design, and analytics, own quarterly goals, improve platform reliability and code quality, mentor engineers, and support on-call and operational monitoring.

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

Manage a team of ML engineers to develop machine learning solutions for underwriting applications. Set technical strategy, mentor engineers, and collaborate with teams across the organization.

Lead development and productionization of fraud prediction models (tabular, graph, behavioral). Build scalable feature pipelines, run experiments, integrate models into real-time/batch systems, monitor model and data health, and collaborate cross-functionally to maintain model reliability and performance.

Lead and manage an ML engineering team building post-origination credit models for repayment, collections, recovery, and loss mitigation. Define technical strategy, design and scale ML solutions, partner with product, engineering and risk, mentor engineers, and operationalize models to minimize loan loss and personalize collections.

Independently validate and monitor machine learning models used for credit underwriting, credit risk, and fraud detection. Identify weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Partner cross-functionally to implement and maintain the Model Risk Management framework and support audit, compliance, and regulatory requests.