Top AI & Machine Learning Jobs

Leads enterprise AI research and development strategy, combining classical and generative AI into production-grade platforms, agentic systems, and customer products. Builds and scales teams of applied scientists and engineers, advances hybrid AI architectures, establishes experimentation and evaluation practices, and embeds responsible AI principles. Partners with senior leaders, academia, and industry to commercialize research and deliver measurable business impact.

Senior Data Scientist partnering with Product, Engineering, Design, Operations, and Clinical teams to define metrics, design experiments, apply causal inference, analyze funnels and cohorts, and build predictive models. The role translates complex data into actionable product and business recommendations, operationalizes validated insights, and establishes standards for experimentation and analytical quality across patient, payer, outcomes, and provider-focused teams.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Designs, develops, and deploys computer vision and machine learning pipeline components for GEOINT data. Responsibilities include synthetic data generation with generative AI, data engineering, model training and production deployment, cloud and Kubernetes workflow operations, system architecture, production support, and briefing teams on project progress and results.

Leads multiple AI/ML engineering teams delivering enterprise AI, generative AI, agentic AI, and automation solutions. Oversees platform strategy, software delivery, responsible AI governance, cloud adoption, and operational excellence. Partners with product and engineering leaders on healthcare solutions, mentors technical teams, and manages AI initiatives spanning claims automation, conversational AI, predictive analytics, and intelligent workflows.

Conduct advanced statistical modeling and Agentic AI research for healthcare applications. Develop, evaluate, optimize, and document LLM and NLP solutions, collaborate on responsible AI compliance, build MLOps data pipelines with engineering teams, and contribute to patents and academic publications.

Design, develop, deploy, and optimize scalable AI/ML solutions, including LLM applications, RAG pipelines, AI agents, and MLOps workflows. Build production-grade ML pipelines, automation frameworks, monitoring, evaluation, and governance capabilities using Python and AWS services. Collaborate with technical and business teams to integrate reliable AI solutions into production systems and continuously improve model performance, scalability, and compliance.

The Principal AI Compiler Engineer designs hardware for AI, ensuring models translate effectively to silicon and perform optimally in 5G applications.

The Principal AI Model Architect's role includes optimizing model architectures for new hardware, translating AI algorithms into hardware specifications, and projecting performance for silicon design.

Design, build, deploy, and operate production AI agents and multi-agent systems for insurance workflows. Responsibilities include agent orchestration, APIs, RAG pipelines, evaluation frameworks, regression testing, observability, production troubleshooting, and reliability improvements. The role requires hands-on Python software engineering, LLM application development, cloud infrastructure, and collaboration in end-to-end product pods.

Build and scale AI-driven analytics and decision-support products for Finance. Responsibilities include prototyping LLM agents, multi-agent workflows, RAG, forecasting, evaluation harnesses, guardrails, data architectures, semantic layers, observability, and data governance. The role also owns AI and data roadmaps, experimentation frameworks, and business-impact measurement while partnering with Finance, engineering, and platform teams.

As the Director of Data Science, you'll mentor teams, oversee projects, drive data-driven decisions, and implement AI and ML solutions while enhancing our data pipeline.

Research and develop agentic AI systems for cybersecurity, including LLM post-training, reinforcement learning, planning, tool calling, retrieval, memory, prompt optimization, and rigorous evaluation. Collaborate with cybersecurity experts, data scientists, and engineers to prototype state-of-the-art methods and transition successful systems toward production. Analyze agent failures, track AI advances, and define research priorities.