Twelve Labs

• Own the video pipeline end-to-end: Architect and implement ingestion → decode → chunking → storage → retrieval → playback, across batch and streaming modes based on AI/ML workflows or media application workflows. • Deep codec & decode mastery: Drive decisions on decode strategies (hardware vs. software, GPU-accelerated pipelines), container format handling (fMP4, CMAF, MKV, TS), and codec support (H.264, H.265, VP9, AV1) with pragmatic cost/quality tradeoffs. • Semantic & heuristic chunking: Work with our ML Research Scientists to design and implement intelligent video segmentation that goes beyond fixed-interval splitting — scene boundary detection, shot change analysis, content-aware chunking that optimizes downstream AI model performance. • Streaming ingestion: Architect low-latency streaming pipelines (HLS, DASH, LL-HLS, WebRTC ingest) that process video in near-real-time, including streaming decode and incremental chunking. • Video storage architecture: Design storage tiers and retrieval patterns optimized for AI workloads — balancing hot/warm/cold access, frame-level random access, and cost at petabyte scale. • Playback & delivery: Ensure video can be served back to users with accurate temporal navigation, supporting time-coded references from AI analysis results. • FFmpeg & media toolchain expertise: Be the internal authority on FFmpeg, libav, and related tooling. Build and maintain custom processing pipelines, filters, and integrations. • Cost engineering: Quantify and optimize cost-per-hour-of-video-processed. Drive decode efficiency through hardware acceleration (NVDEC, VA-API), pipeline parallelism, and intelligent resource allocation. • Cross-team technical leadership: Partner with ML teams on how video is preprocessed for model consumption, with platform teams on infrastructure, and with product on customer-facing media capabilities. • Standards & best practices: Establish video engineering standards, author reference implementations, and mentor engineers across teams on media fundamentals.

• Build fluid, highly polished UIs with React and TypeScript that feel fast and intentional • Own interaction design implementation — microinteractions, transitions, animations, and motion using Framer Motion, React Spring, or GSAP • Collaborate closely with the design team on rapid ideation and prototyping of new interaction patterns • Translate high-fidelity Figma designs into production interfaces with pixel-level accuracy • Build and maintain a component library and design system that enforces consistency and craft across the product • Optimize rendering performance for complex canvases, timelines, and media-rich views • Write reliable unit, integration, and E2E tests for complex interactive UI workflows • Use AI-assisted development tools thoughtfully to accelerate implementation while maintaining quality • Prototype interaction concepts quickly to validate design direction before committing to full implementation • Advocate for and implement the micro-details that elevate product feel — loading states, error handling, empty states, and transitions between views

• Design and build infrastructure that deploys TwelveLabs models across multiple cloud and data platforms, accounting for differences in compute hardware, networking, APIs, and operational models • Own direct integrations into partner products — implementing the orchestration, data flow, and API surfaces that connect TwelveLabs models to partner-side functionality • Design and evolve CI/CD automation systems — including validation and deployment pipelines that reliably ship new model versions across platforms without regressions • Design interfaces and tooling abstractions across platforms that enable consistent deployment, reduce per-platform complexity, and scale as we add new partners • Implement API-level features and changes that require understanding model component behavior — routing, request handling, response formatting — without modifying model internals • Contribute to capacity planning and autoscaling strategies that dynamically match supply with demand across platform deployments • Analyze observability data across platforms to identify performance bottlenecks, cost anomalies, and regressions — and drive remediation based on production workloads • Collaborate with platform partner engineering teams to resolve operational issues, align on API contracts, and stand up end-to-end serving on new platforms

• Adapt TwelveLabs' video understanding and multimodal models for government-specific use cases (defense, intelligence analysis, federal records management) • Run training and fine-tuning experiments on cutting edge GPU infrastructure • Develop supervised fine-tuning pipelines tailored to government-specific datasets and annotation workflows • Design rigorous evaluation frameworks, including domain-specific benchmarks and operational performance metrics, that are tailored to public sector requirements • Work closely with Solutions Engineering and the engineering team to translate customer requirements into technical implementations

• Embed directly with federal, state, and local government customers to understand their video analysis workflows, pain points, and operational requirements • Design and implement video understanding solutions using TwelveLabs' platform (semantic search, scene detection, object tracking, activity recognition, summarization) on government networks • Deploy our multimodal AI models in AWS GovCloud, Azure Government, FedRAMP environments, classified networks, and on-premises secure facilities • Build custom pipelines integrating TwelveLabs APIs with government systems like Palantir Foundry, Salesforce, SharePoint, and agency-specific platforms • Serve as the primary technical point of contact for your assigned customers—you own their success • Rapidly prototype solutions using real customer data and operational requirements • Translate mission needs into technical implementations, whether that's building video search interfaces, automated monitoring workflows, or content analysis systems • Troubleshoot complex production issues in secure environments and maintain high system availability • Manage engagement post deployment in air-gapped networks, classified systems, and secure facilities • Prepare and deliver security documentation for ATOs (Authority to Operate), CTFs (Certification to Field), and RMF compliance • Work within NIST, FedRAMP, FISMA, and federal security frameworks • Interface with government security teams, ISSOs, and AOs to ensure compliant deployments • Bring field learnings back to product and engineering teams to shape the roadmap • Identify patterns across customer deployments that can be productized into platform features • Contribute to internal tooling, deployment automation, and reference architectures • Collaborate with ML engineers to optimize model performance for government use cases • Build trusted relationships with government program managers, technical leaders, and end users • Present technical solutions to senior government leadership and agency stakeholders • Understand customer missions deeply enough to propose solutions they didn't know were possible • Represent TwelveLabs at government conferences, industry days, and agency briefings

• Help define the design language and interaction model across every TwelveLabs product surface: applications, platform, APIs, brand, launch materials • Invent interaction and design patterns for a new paradigms across all scales from single frames to entire video corpuses • Prototype relentlessly. Test with developers in production, and enterprise teams working across millions of hours of footage • Build the design system by shipping

• Partner with the lead security engineer to identify risk, build controls, and drive security initiatives across the organization • Perform security assessments, penetration testing, and code reviews to find and remediate vulnerabilities across our applications and infrastructure • Integrate security tooling and controls into CI/CD pipelines and software development workflows, shifting security left without slowing teams down • Help design and enforce cloud security architecture across our AWS environment, including IAM, WAF, network segmentation, encryption, and runtime threat detection • Develop and continuously improve security monitoring, logging, and alerting capabilities to support incident detection and response • Participate in the security on-call rotation and assist with incident response and investigation as needed • Contribute to compliance efforts across SOC 2, GDPR, CMMC/NIST, etc., including audit support and policy development • Help secure AI/ML pipelines, model training infrastructure, and model artifacts including weights and deployment code • Build and maintain internal security tooling and automation to scale our program without scaling headcount • Partner with engineering, infrastructure, and IT to embed security practices directly into development and delivery workflows

• Build and lead the Partner Engineering team, defining its charter, operating model, and success metrics. • Develop and execute the global partner engineering strategy across key ecosystems (cloud providers, AI platforms, ISVs, and enterprise integrators). • Guide technical pre- and post-sales efforts, ensuring seamless partner onboarding, solution design, and integration with Twelve Labs APIs. • Collaborate cross-functionally with Product, Engineering, and GTM leaders to align on technical roadmaps and market opportunities. • Run enablement programs for partner technical teams (e.g., cloud solution architects, AI/ML teams) to integrate and scale Twelve Labs technology within their environments. • Drive co-innovation projects, developing proof-of-concept integrations that demonstrate the value of Twelve Labs technology and inspire broader adoption. • Represent Twelve Labs externally—speaking at conferences, publishing technical content, and cultivating thought leadership in the AI and video intelligence space. • Monitor the competitive landscape and emerging technologies to inform product strategy and identify high-leverage opportunities for integration. • Partner with leadership to shape go-to-market models, pricing structures, and joint business plans with strategic partners. • Serve as the customer’s technical advocate, synthesizing partner feedback into actionable insights for the product roadmap.

• Generate qualified pipeline in emerging verticals. • Conduct market research and account planning. • Execute multi-channel outreach campaigns. • Qualify inbound and outbound leads. • Collaborate with Account Executives. • Partner with Marketing. • Educate prospects on video AI capabilities. • Track and report on pipeline metrics. • Contribute to go-to-market strategy.

• Own the Demand Engine • Design, execute, and optimize multi-channel demand generation programs (paid media, content syndication, ABM, email, events, webinars) that deliver measurable pipeline impact • Build and manage the marketing funnel from MQL through SQL, working closely with Sales to ensure seamless handoffs and feedback loops • Develop audience segmentation strategies across our key personas: technical developers ("Dev Dorothy"), media operators ("Media Meredith"), and business decision-makers ("Biz Brad") • Drive Pipeline Mathematics • Set and hit quarterly pipeline generation targets tied to revenue goals • Build attribution models and reporting dashboards that connect marketing activity to closed-won revenue • Continuously test and iterate: A/B testing creative, channels, messaging, and targeting to improve conversion at every stage • Scale Our Enterprise Motion • Partner with Enterprise Marketing to develop account-based programs targeting strategic accounts • Create integrated campaign strategies that span digital, field events, and partner co-marketing • Work with Product Marketing to translate technical capabilities into compelling value propositions for each vertical • Build the Foundation • Establish scalable processes, playbooks, and infrastructure for demand generation as we grow • Manage relationships with agencies, vendors, and technology partners (HubSpot, LinkedIn, Google, etc.) • Collaborate with Marketing & Data Operations to ensure clean data, accurate attribution, and actionable insights