Top Remote Tech Jobs

Coordinates information security human risk programs, including training deployments, simulated phishing campaigns, onboarding, targeted learning, awareness communications, metrics, and audit-ready documentation. Drafts behavior-focused educational materials, tracks initiative milestones and risks, reviews campaign results, identifies process improvements, and collaborates with Information Security, HR, Legal, Communications, and business stakeholders.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Leads the Safety Engineering Mobile Client team, setting technical direction and delivering secure, scalable mobile systems for account integrity, authentication, reporting, and compliance. Responsibilities include coaching and hiring engineers, driving complex cross-functional initiatives, improving engineering quality and operational excellence, partnering with Product, Legal, Trust & Safety, and backend teams, and balancing immediate delivery with long-term platform investments. The manager also promotes responsible use of AI-assisted development while maintaining rigorous security, performance, and code-quality standards.

Own Rubrik’s customer story and CXO Visionaries programs from recruitment and discovery through content production, events, awards, and executive engagement. Develop technically grounded customer narratives, manage editorial workflows and external vendors, track story impact, and partner with sales, PR, product, engineering, alliances, and events teams. Build scalable processes and quality standards while translating cybersecurity and data resilience outcomes into compelling proof points for senior business and technology audiences.

Lead the architecture and development of large-scale machine learning systems, distributed training infrastructure, ranking systems, and production ML services. Drive complex initiatives from concept through deployment, establish engineering standards, partner with product and engineering leadership, and mentor data scientists and engineers. Build reliable, low-latency, high-availability AI platforms using advanced ML frameworks, accelerators, and big data technologies.

Lead technical presales and solution architecture for HPE enterprise storage across U.S. Federal accounts. Advise customers and systems integrators, design storage architectures, conduct discovery, sizing, demonstrations, proofs of concept, competitive positioning, and proposal support. Navigate Federal acquisition, security, compliance, and modernization requirements while coordinating internal engineering, product, services, and partner teams. The role requires deep storage expertise, Federal-sector experience, customer-facing technical sales capability, U.S. citizenship, and travel exceeding 50%.

Design, deploy, and operate multi-vendor, multi-region hybrid cloud data center networks. Drive automation-first deployments using IaC and scripting, improve deployment quality/velocity, integrate automation into capacity workflows, build visibility/tools, maintain production reliability, and participate in 24x7 on-call rotations.

Leads Unanet’s internal data strategy and architecture for enterprise AI. Defines the shared data foundation, target architecture, governance standards, roadmap, and reusable approaches for AI assistants, agents, and workflows. Evaluates platforms and integration options, validates technical solutions, and coordinates IT, Security, Legal, Engineering, business owners, and system specialists. Advises senior leaders, establishes data quality and stewardship requirements, and helps determine when data is ready to support scalable AI initiatives.

Build and maintain Customer Success and Implementation playbooks, SOPs, training programs, knowledge bases, presentation materials, and scalable workflows. Partner cross-functionally to communicate process and product changes, improve automation and efficiency, identify customer risks, and measure enablement effectiveness. Support implementation, launch readiness, customer health, escalations, business reviews, renewals, and expansion.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.