Top AI & Machine Learning Jobs

Build, deploy, govern, and improve AI agents for HR workflows using Gemini Agent Builder, Claude, Codex, and MCP integrations. Partner with HR subject matter experts to redesign processes, transfer technical capabilities, monitor adoption, diagnose failures, and drive stakeholder adoption. Collaborate with AI and engineering teams on integrations and governance. The role requires hands-on GenAI building experience, technical fluency, evaluation expertise, change management, and sensitivity to people-data risks.

Build and operate production LLM-powered agents and machine learning systems for customer risk identification and threat detection. Translate security heuristics into agent logic, collaborate with threat researchers, develop data requirements and pipelines, evaluate precision and recall, and deploy solutions through CI/CD. The role also requires production reliability ownership, observability, incident debugging, cloud infrastructure expertise, and senior staff-level technical leadership.

Develop production LLM-powered agents and machine learning solutions for customer risk identification and threat detection. Translate threat-research methods into agent logic, build data requirements and pipelines, evaluate precision and recall, backtest and tune models, and deploy and monitor reliable solutions using CI/CD. Collaborate with threat researchers and data engineers while addressing production reliability, observability, data quality, cost, latency, and accuracy.

Build and scale the agentic layer of an AI-native product operations platform. Design and ship production-grade AI systems using FastAPI, NextJS, and Postgres. Implement RAG, embeddings, vector retrieval, prompt engineering, orchestration patterns, guardrails, and evaluation frameworks. Partner with product stakeholders to deliver cost- and throughput-optimized solutions in a high-velocity, rapid-iteration environment.

Lead architecture and delivery of large-scale AI/ML infrastructure, LLM serving platforms, and agentic workflows for cloud-scale cybersecurity. Build secure, observable, reliable production systems; drive cross-functional architecture initiatives; mentor engineers; and translate complex distributed-system designs into business outcomes. The role requires expertise in distributed systems, cloud-native microservices, LLMs, production AI/ML operations, security architecture, and technical leadership.

As a Sr. Staff Machine Learning Engineer, you will drive the development of cloud security features, leveraging AI/ML to solve real-world challenges and optimize production systems.