Top AI & Machine Learning Jobs

Build and deploy AI-native healthcare products for clinicians and patients. Develop LLM-powered systems, agents, tools, retrieval workflows, and production infrastructure. Own features from experimentation through deployment, evaluation, monitoring, and iteration. Collaborate with clinicians and engineers to identify impactful healthcare applications while optimizing reliability, latency, cost, and failure handling.

Research Fellows will validate AI-generated paper reproductions, simulations, and results; map research taxonomies; define standards for high-quality AI-assisted science; and create benchmark tasks and evaluations for frontier AI models. Fellows will contribute to a publicly released benchmark paper and open evaluation set as lead or first authors. The contract offers flexible, remote global work for current STEM PhD students or postdocs with published research and hands-on AI agent experience.

Lead the architecture and development of Jerry.ai’s next-generation AI agents and workflow automation platform. Build conversational and computer-use agents, document validation, voicebot interactions, and integrations with external insurance systems. Evaluate emerging AI technologies, deploy production LLM systems, and improve reliability, latency, cost, evaluation, and failure handling. Partner with OpenAI on next-generation voice models and automation capabilities.

Leads the design, development, deployment, and governance of production machine learning and GenAI systems on Azure Databricks. Establishes MLOps standards, builds MLflow and CI/CD foundations, architects RAG and LLM solutions, monitors model performance and cost, and mentors ML and data engineers. Partners with product, risk, governance, legal, and security teams to ensure models are reliable, explainable, fair, documented, and compliant.

Designs, develops, and deploys production AI applications for life insurance, including LLM workflows, RAG solutions, intelligent agents, copilots, and prompt evaluation frameworks. Builds React front ends, Python Flask APIs, microservices, and enterprise integrations supporting underwriting, claims, servicing, customer experience, and agent productivity. Collaborates with architecture, cloud, data, and product teams while applying DevOps, governance, security, scalability, and responsible AI practices.

Provides technical leadership for full-stack engineering and applied AI initiatives. Designs and delivers resilient microservices, event-driven systems, AI-augmented workflows, and agentic solutions using cloud, orchestration, retrieval, and model technologies. Evaluates AI use cases, establishes production monitoring and governance practices, mentors engineers, influences technology strategy, and ensures solutions meet reliability, security, performance, and operational standards.

Own the strategy and delivery of AI-enabled products for enterprise functions. Identify and prioritize use cases, build business cases, manage roadmaps and backlogs, define requirements, and guide solutions from discovery through adoption. Partner with technical and business teams to assess data readiness, governance, privacy, security, risk, and model quality. Lead change management, training, communication, and performance measurement while connecting AI initiatives to ROI, efficiency, quality, and user adoption.

Lead the architecture and production deployment of next-generation AI agents and workflow automation. Own conversational interfaces, voicebots, computer-use agents, document validation, transaction orchestration, and external system integrations. Evaluate emerging AI technologies, optimize LLM systems for reliability, latency, cost, and failure handling, and partner with OpenAI on advanced voice and automation models.

Conduct applied research and develop scalable systems for dynamic context graphs, knowledge representation, graph machine learning, information extraction, retrieval, and LLM-based reasoning. Build methods for entity and relationship resolution, temporal reasoning, provenance, uncertainty, and agentic workflows. Design evaluation benchmarks for graph quality and reasoning reliability, prototype systems with engineering teams, and contribute through publications, patents, technical strategy, and mentorship.

Design and implement traditional machine learning and generative AI capabilities for electronic warfare products. Conduct research and development on emerging AI trends, support product design, ensure quality assurance, and transition capabilities into production. The role requires clearance, programming ability, and education or equivalent experience in machine learning or artificial intelligence. Prior intelligence community or Department of War experience is preferred.