Top Hybrid Jobs

Own the CRM lifecycle strategy and campaign calendar across email, push, SMS, and in-app channels. Build personalized automated and ad-hoc campaigns, manage segmentation and testing, troubleshoot deployments, analyze performance, and present insights to stakeholders. Partner with Product, Design, Engineering, Data, and Brand to improve engagement, retention, onboarding, and reactivation.

Advises enterprise MongoDB customers on complex technical deployments, driving adoption, retention, revenue realization, advocacy, and long-term business outcomes. Manages strategic account portfolios, leads business reviews, forecasts customer health, coordinates cross-functional resources, and identifies expansion opportunities. Builds senior stakeholder relationships, influences product strategy through customer insights, mentors teammates, supports hiring and onboarding, and applies AI fluency to improve customer engagement and operational efficiency.

Own full-cycle, net-new enterprise sales for the MapQuest mapping platform. Build outbound pipeline across targeted verticals, sell location APIs and SDKs to technical executives, manage technical evaluations and proofs of concept, negotiate enterprise contracts, and close multi-year deals. Partner with Solutions Architecture, procurement, product, and company leadership while providing market feedback. This is a remote individual-contributor role with prospect and event travel.

Build and lead MapQuest APIs' technical selling function for enterprise customers. Conduct technical discovery, create proofs using customer geospatial data, win competitive evaluations, develop migration tools and code samples, and own technical customer sign-off through implementation handoff. The role combines solutions architecture, development, technical sales, and geospatial investigation while supporting enterprise API revenue growth.

Responds to cybersecurity incidents by triaging alerts, coordinating containment, building automation, improving detection capabilities, and leading post-incident reviews. Partners with threat intelligence and detection engineering teams, participates in incident command, conducts tabletop and purple team exercises, maintains runbooks, and supports a light on-call rotation. Requires cloud security expertise, knowledge of threat actor tactics, and programming experience for incident response and forensic tools.

Targets and closes new business with Fortune 1000 and other strategic customers. Builds enterprise relationships, uncovers cloud-scale technology challenges, sells Datadog solutions, negotiates pricing, expands existing accounts, develops pipelines, and maintains accurate forecasts. Requires enterprise technology sales experience, strong prospecting and closing skills, large-deal experience, and regular travel to client sites.

Own the vision, strategy, and execution of Datadog RUM features that improve customer onboarding, product adoption, experience consistency, and frontend performance intelligence. Analyze quantitative and qualitative customer insights, define requirements and roadmaps, collaborate with engineering, design, data science, marketing, customer success, and technical writing, and communicate releases to technical and executive audiences.

Architect and develop production-grade GenAI systems, including LLM-powered agents, RAG pipelines, tool integrations, multi-agent coordination, memory systems, and chatbot experiences. Lead AI productization across engineering, product, and research teams; establish architecture, infrastructure, observability, coding standards, and delivery practices. Mentor engineers, guide technical roadmaps, and ensure scalable end-to-end deployment across frontend, backend, and ML components.

Lead development and production deployment of machine learning systems for time-series anomaly detection in connected-device environments. Build streaming and MLOps pipelines, tune alerts, monitor model drift, and integrate AI systems with LLM-based assistants. Guide architecture, tooling, observability, and production readiness while mentoring and managing applied AI and full-stack engineers. Collaborate across engineering, product, data, and research teams.

Leads qualitative and quantitative market research across product, marketing, and strategy. Owns research programs from scoping through execution, analysis, synthesis, and stakeholder presentation. Develops segmentation, concept testing, journey mapping, brand tracking, and usage studies; translates findings into strategic recommendations and facilitates activation workshops. Manages research vendors, maintains insight dashboards, upholds methodological standards, and influences product roadmaps, marketing campaigns, and business decisions.

Own customer security reviews, questionnaires, RFP responses, and due-diligence requests. Support SOC 2 Type II and ISO audits by coordinating evidence, auditor interactions, and remediation. Maintain security, privacy, and AI governance policies and controls, including frameworks such as ISO/IEC 42001 and NIST AI RMF. Partner with Product, Engineering, DevOps, Security, Sales, and Legal to explain cloud controls, manage customer trust requirements, and address control gaps.

Build and maintain internal developer platforms, shared services, CI/CD pipelines, cloud infrastructure, observability systems, automation, and self-service tooling. Partner with engineering teams to improve development workflows, deployment reliability, operational efficiency, and developer productivity. Lead foundational technical projects from architecture through rollout and ongoing maintenance while balancing scalability, reliability, and developer experience.

Prepares financial reports, analyzes operating budgets and trends, develops forecasts, performs variance analysis, reviews financial statements, and identifies cost-saving opportunities to support business planning and performance goals.

Own the strategy, roadmap, and execution for a major area of Superhuman Go, an AI assistant. Partner with engineering, design, research, data science, and go-to-market teams to develop trusted AI experiences, platform capabilities, agents, integrations, and cross-device workflows. Define user journeys, metrics, experiments, and priorities while translating complex technical capabilities into intuitive products. Drive clarity and execution across interconnected teams and Superhuman’s product suite.

Develop and monitor fraud-detection models and pattern-recognition algorithms for lending products. Analyze large datasets using Python and SQL, identify emerging fraud trends, and collaborate with Fraud Operations to refine features and reduce false positives and negatives. Conduct ad hoc analyses, recommend risk and verification strategies, communicate findings to cross-functional partners, and support improvements to underwriting and verification processes.