Principal Software Engineer, Video Engineering

WHO WE ARE At Twelve Labs, we are pioneering the development of cutting-edge multimodal foundation models that have the ability to comprehend videos just like humans do. Our models have redefined the standards in video-language modeling, empowering us with more intuitive and far-reaching capabilities, and fundamentally transforming the way we interact with and analyze various forms of media. With a remarkable $107 million in Seed and Series A funding, our company is backed by top-tier venture capital firms such as NVIDIA’s NVentures, NEA, Radical Ventures, and Index Ventures, and prominent AI visionaries and founders such as Fei-Fei Li, Silvio Savarese, Alexandr Wang and more. Headquartered in San Francisco, with an influential APAC presence in Seoul, our global footprint underscores our commitment to driving worldwide innovation. We are a global company that values the uniqueness of each person’s journey. It is the differences in our cultural, educational, and life experiences that allow us to constantly challenge the status quo. We are looking for individuals who are motivated by our mission and eager to make an impact as we push the bounds of technology to transform the world. Join us as we revolutionize video understanding and multimodal AI. ABOUT THE ROLE Most video engineering roles at Netflix, YouTube, or Twitch optimize for human playback — better compression, lower bitrate, smoother streaming. At Twelve Labs, video is processed for machine understanding. The tradeoffs are fundamentally different: we optimize for AI model performance, not just perceptual quality. This is a rare opportunity to define how video is engineered for intelligence — not just delivery. As the Principal Software Engineer, Video Engineering, you will own the architecture and implementation of Twelve Labs' video processing pipelines — from byte ingestion through decode, chunking, storage, and playback — ensuring it is fast, cost-efficient, and purpose-built for AI-native video intelligence at scale. You will be the internal subject matter expert on all things related to video engineering. IN THIS ROLE YOU WILL: - 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. YOU MAY BE A GOOD FIT IF YOU HAVE: - 12+ years in software engineering with 7+ years focused on video/media engineering in production systems processing video at scale. - Deep FFmpeg expertise: Not just CLI usage — understanding of libavcodec, libavformat, filter graphs, custom demuxers/decoders, and performance tuning. - Codec internals knowledge: H.264/H.265 bitstream structure, AV1 adoption tradeoffs, hardware decode paths, quality metrics (VMAF, SSIM, PSNR). - Streaming protocol fluency: HLS, DASH, LL-HLS, WebRTC. Experience with live/real-time ingest pipelines. - Systems engineering depth: Comfortable in C/C++, Rust, or Go for performance-critical media code; Python for pipeline orchestration. Can reason about memory layout, SIMD, GPU pipelines.