Top AI & Machine Learning Jobs

The Machine Learning Researcher will develop AI agents for private equity-backed workflows, focusing on ML cycle efficiency and decision-making.

Lead data science for defense clients: perform data engineering, mining, exploratory/predictive/statistical analysis; build ML/AI tools (recommendation engines, automated scoring); manage large, disparate data sources using R/Python/SQL; create visualizations in Power BI/Tableau; support Multi-INT analytics and provide senior-level analytic products for the defense cover program.

Own and build large, critical parts of an AI product: runtime/agent orchestration, search/retrieval and vector embedding pipelines, evals and model quality, backend services, and cloud infrastructure to deliver production-grade systems.

Lead and execute the most complex enterprise deployments of real-time LLM agent systems. Architect integrations, debug production voice/telephony issues, mentor FDEs, build reusable deployment tooling, and drive product feedback to core engineering.

Develop Python adapters, API integrations, and AI agent workflows using OpenAI/Claude and LLM tooling. Build AI-powered documentation, calling/communication agents, test and optimize solutions, and collaborate with senior engineers on healthcare AI projects.

Lead and own the most complex enterprise deployments of real-time AI agents end-to-end. Architect systems, build integrations and tooling, mentor FDE engineers, debug production incidents (telephony, latency, integrations), and translate deployment learnings into product improvements while interfacing directly with customer engineering leadership.

Design, build, and deploy LLM-powered agents and retrieval systems that autonomously execute multi-step healthcare tasks; create evaluation and monitoring infrastructure to measure and improve model quality; ship copilots and assistive features; iterate from production signals and make scalable architectural decisions.

Build and own the analytics pipeline and semantic layer, define canonical product and business metrics, evaluate AI/agent performance, support pricing and profitability analysis, design dashboards and quality checks, translate ambiguous questions into analyses, and hire/scale the data team.

Build, deploy, and operate production ML systems (recommendation, ranking, personalization, optimization). Own feature engineering, training pipelines, online inference, experimentation, monitoring, retraining, and MLOps. Partner cross-functionally, mentor engineers, and deliver measurable business outcomes on a large-scale data platform.

Lead development of an AI-native GEOINT mission management platform that translates operator intent into physically valid collection plans. Own technical vision, build LLM-powered agents, integrate orbital mechanics and sensor models, develop MCP servers and evaluation suites, mentor an engineering team, and drive prototype-to-production for accredited IC/DoD deployments.