Top AI & Machine Learning Jobs

Leads the technical vision and architecture for AI-powered exploitability analysis in cybersecurity. Defines advanced modeling approaches, enterprise-scale engineering standards, AI safety and governance practices, and portfolio-wide architecture. Guides multiple teams without direct authority, mentors senior engineers, represents technology to executives and customers, and delivers production AI/ML products using LLMs, agents, retrieval, probabilistic scoring, and graph technologies.

Own the architecture and production delivery of a novel AI-powered exploitability engine. Lead probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drive zero-to-one technical direction, architecture, code reviews, cross-functional partnerships, and mentorship. Build scalable systems using distributed technologies, cloud-native development, APIs, databases, and machine learning while establishing defensible engineering decisions and evaluation metrics.

Own and deliver a major subsystem of ServiceNow’s exploitability analysis engine, such as probabilistic scoring, exposure graphs, entity resolution, or calibration. Lead architecture, design and code reviews, production delivery, metrics, AI safety guardrails, and technical direction. Partner with product, security R&D, and SecOps while mentoring engineers and guiding ambiguous AI/ML problems to scalable, reliable production systems.

Build and deploy end-to-end GenAI and ML systems for strategic customers, including RAG pipelines, agentic workflows, evaluation harnesses, guardrails, observability, and production services. Lead AI discovery workshops, translate business problems into technical solutions, collaborate with engineers, create reusable frameworks, influence product strategy, and enable customer adoption. The role requires strong software engineering, ML, MLOps, responsible AI, platform architecture, and customer-facing delivery skills, with travel up to 30%.

Build and operate reliable production systems for machine learning models, agentic workflows, and self-learning capabilities. Responsibilities include ML lifecycle automation, continuous delivery, safe deployments, feedback loops, observability, SLOs, distributed workload optimization, incident response, platform automation, and cloud infrastructure standards. The role provides staff-level technical leadership across ML, data, product, and platform engineering teams.

Leads AI engineering enablement by developing onboarding programs, curricula, workshops, technical labs, and engineering forums. Partners with technical experts to identify skill gaps, build practical learning experiences, measure program effectiveness, and improve engineering capabilities. The role requires strong software engineering judgment, hands-on AI/ML expertise, teaching and facilitation skills, and the ability to engage senior technical audiences.

Leads hands-on implementation and adoption of ServiceNow’s agentic AI capabilities. Owns AI architecture decisions, delivers demonstrations and proof-of-concepts, advises stakeholders and customers, develops enablement and training programs, and drives measurable business outcomes. Partners with sales, product, strategists, engineers, and customer teams while contributing to thought leadership and evolving AI initiatives.

Build and operate production AI-native software for sales and order management. Design agentic workflows, prompts, tools, guardrails, conversational experiences, automated evaluations, and safety controls. Deliver full-stack applications, APIs, data models, and integrations across quoting, billing, contracts, cases, and fulfillment. Monitor reliability, investigate failures, mentor engineers, and collaborate with product, design, and conversation teams.

Design, build, optimize, and scale machine learning infrastructure for training, evaluating, deploying, and serving large language models. Develop reusable workflow automation abstractions, distributed training and inference pipelines, model evaluation and monitoring frameworks, and latency optimizations. Collaborate with machine learning, data infrastructure, analytics, engineering, and product teams while establishing best practices for ML and data engineering.

Lead research in agent learning and recursive self-improvement for enterprise AI agents. Design post-training methods, agent harnesses, training environments, evaluations, and distributed pipelines across language and multimodal systems. Analyze failures, develop training signals, run rigorous experiments, and transition validated improvements into reliable production systems. Collaborate with research, engineering, infrastructure, security, and product teams while communicating results through publications, patents, reports, and open-source work.