Top AI & Machine Learning Jobs

Architect, build, deploy, and scale production AI agents and platforms for legal workflows. Develop secure, reliable, and cost-effective AI solutions; prototype and evaluate agents; optimize performance; redesign business processes; and collaborate with Legal, Data, Security, Privacy, and Engineering teams. Contribute to AI training, governance, responsible AI adoption, and enterprise standards while translating technical capabilities into measurable business outcomes.

Build and deploy production-grade AI and machine learning solutions for marketing and sales, including LLM agents, data-quality pipelines, semantic search, RAG, analytics, and integrations with Salesforce, Marketo, and Adobe platforms. Define evaluation and governance practices, monitor AI services, implement privacy controls, and collaborate with platform teams. Train and mentor technical and business stakeholders on responsible AI adoption while supporting DevOps, incident response, and operational runbooks.

Lead research and development of production-grade generative AI agents and large language models. Set technical direction for model architectures, training, deployment, evaluation, and optimization at scale. Mentor data scientists, guide cross-functional technical strategy, establish engineering standards, and collaborate with senior leadership. Drive innovations in reasoning, summarization, and interactivity while representing technical work through publications, speaking engagements, and thought leadership.

Leads a team developing AI capabilities for CrowdStrike’s cross-platform endpoint sensor. Owns the AI-on-endpoint roadmap, including machine learning model operationalization, telemetry data pipelines, on-device inference, architecture, feature delivery, and cross-functional collaboration. Manages hiring, mentoring, engineering quality, testing, performance, and releases across Windows, macOS, and Linux sensor platforms.

Leads the AI Solutions organization, including hiring, technical direction, intake, prioritization, staffing, architecture review, delivery, and production readiness. Converts internal AI requests into build plans, allocates virtual engineering and product teams, establishes standards for security, privacy, reliability, evaluation, and cost, and ensures successful handoffs. The role also develops reusable AI solution patterns and partners with executive, business, platform, architecture, privacy, and security stakeholders.

Leads a team researching and evaluating AI, LLM, and agentic systems for cybersecurity workflows. Defines evaluation strategies, benchmark datasets, metrics, methodologies, and reproducible testing pipelines grounded in SOC operations. Assesses model accuracy, robustness, reliability, and operational effectiveness across incident response, threat hunting, alert triage, and investigations. Partners with engineering, product, and threat research teams to translate findings into improvements and communicates results to technical and executive stakeholders.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Build and operate production-grade agentic AI systems and backend microservices for Charlotte AI and Agentic SOC products. Responsibilities include LLM integrations, agent orchestration, model evaluation, MLOps pipelines, inference scaling, security safeguards, observability, and production reliability. The role provides technical leadership, architecture direction, mentoring, and cross-functional collaboration while remaining hands-on with code.

Define and drive CrowdStrike’s company-wide AI and agentic architecture. Design production AI systems, shared platforms, lifecycle workflows, and developer tooling across products and business units. Improve performance, resilience, scalability, observability, governance, security, and auditability across cloud and edge environments. Build reference implementations, guide architectural decisions, resolve complex engineering challenges, mentor technical leaders, and align senior stakeholders around AI strategy and implementation.