Top AI & Machine Learning Jobs

Lead a small AI engineering team (4–8), own end-to-end delivery for an AI product domain, stay hands-on with architecture and code, build production LLM/IDP/agentic systems, ensure observability and reliability, mentor engineers, and collaborate cross-functionally to deliver scalable AI features for insurance customers.

Build core primitives and runtime for Eve, Vercel's framework for production AI agents. Implement filesystem conventions, compiler, integrations (chat channels, connectors), developer tooling, observability/evaluation, and collaborate with platform teams and customers to improve agent reliability and DX.

Design, train, and implement domain-specific LLMs for automation in restoration companies. Collaborate with teams, coordinate AI engineering, and ensure model reliability.

Design and build AI-native, high-performance distributed backend platforms for IT/OT/cybersecurity. Integrate LLMs and ML into production workflows, develop data pipelines and real-time processing, mentor engineers, and drive architectural direction and AI-assisted engineering practices.

Lead strategic deployment of Hebbia's AI platform across global financial institutions by applying LLMs to complex legal and banking workflows, advising customers, driving value-backed land-and-expand growth, informing product and commercial strategy, and optimizing post-sales processes to scale adoption and ROI.

Design, build, and deploy production-scale ML models and ranking systems for personalization and advertising. Take end-to-end ownership of models and services, implement evaluation harnesses and automated tests, and maintain data pipelines using Hadoop/Spark/Kafka. Collaborate across teams and present technical findings, while ensuring scalable, reliable system architecture and rapid iteration using AI-assisted coding tools.

Serve as the final technical authority for platform escalations: diagnose root causes using logs and system analysis, validate platform behavior pre-release, detect systemic incidents, drive technical resolution for Sev1/Sev2 events, and build diagnostic tooling and automation while mentoring the support function.

Design, evaluate, and productionize LLM-based AI products for oncology data. Create custom evaluations, analyze model outputs, collaborate with clinicians and engineers, and support product improvement, monitoring, and stakeholder communication.

Build, iterate, and evaluate production underwriting models (DecisionAssist); own feature engineering and predictive/risk modeling; design and analyze A/B tests and experiments; perform analytics and ad-hoc research in Snowflake/dbt; collaborate with Product and Engineering to translate rental risk into models and clear recommendations.

Build and maintain AI platform infrastructure, connectors, API integrations, and custom AI-powered applications. Implement authentication, data pipelines, and tooling to embed LLM capabilities across teams. Contribute code, documentation, reviews, and enablement while ensuring security and compliance for government requirements.