Top AI & Machine Learning Jobs

Build and scale Moveworks’ generative AI conversation engine for enterprise use. Responsibilities include designing scalable APIs, optimizing low-latency multilingual dialog systems, developing infrastructure for model customization, implementing logging and tracing frameworks, improving observability, and collaborating with ML, application engineering, product, and support teams. The role also champions engineering best practices, system robustness, performance optimization, and rapid innovation.

Lead end-to-end enterprise AI proof-of-concept engagements on ServiceNow, from discovery and scoping through working prototypes and executive demonstrations. Design AI agents, Now Assist solutions, integrations, evaluation criteria, and reusable implementation assets. Partner with sales and delivery teams, lead AI engineers, facilitate customer workshops, articulate business value, and ensure successful handoff to implementation teams.

Leads enterprise AI proof-of-concept engagements on ServiceNow, from discovery and scoping through working prototypes and implementation handoff. Partners with executives, sales teams, and AI engineers to identify use cases, design architectures, configure Now Assist and AI agents, integrate enterprise data, define success metrics, and deliver measurable outcomes within short timelines. Builds reusable prompts, agent templates, integration patterns, and reference solutions while advising customers on AI governance and enterprise workflow automation.

Lead customer-facing design, deployment, and operation of agentic AI solutions on the ServiceNow platform. Analyze customer use cases, architect integrations and guardrails, validate reliability and safety, and guide production deployments. Establish reusable technical standards, reference architectures, and deployment patterns for the Forward Deployed Engineering group. Mentor engineers, advise customer stakeholders, and relay field insights to product and engineering teams.

Leads advanced AI research on enterprise agentic systems, focusing on tacit knowledge, continual learning, resilience, safety, threat detection, privacy, responsible AI, and agent evaluation. Defines research agendas, publishes influential work, validates hypotheses against real deployments, develops production-quality research software, and provides scientific leadership across research, engineering, and product teams.

Lead product vision and execution for an MVP autonomous agent platform, prototyping and shipping agentic capabilities. Own roadmap for orchestration, memory, action execution, and secure governance. Drive adoption, define metrics, partner with engineering, GTM, and open-source ecosystem, and translate frontier AI research into scalable platform primitives.

Own end-to-end AI and machine learning initiatives for cybersecurity vulnerability management, from research and prototyping through scalable production deployment and monitoring. Build Python-based ML pipelines on AWS and Spark, integrate LLM and generative AI capabilities, and develop solutions for automated detection, prioritization, and remediation. Collaborate with researchers, data engineers, and product leaders to address complex cybersecurity problems.

Leads the architecture and production delivery of a novel AI-powered exploitability analysis engine. Owns probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drives zero-to-one development, technical direction, architecture and code reviews, cross-functional partnerships, and mentorship. Requires deep expertise in production AI/ML, LLMs, distributed systems, cloud-native development, probabilistic modeling, and scalable architectures, with strong security experience preferred.

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.