Principal Software Engineer, Video Engineering

TwelveLabs

12d

About The Position

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.

Requirements

• 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.
• Storage & retrieval at scale: Experience designing video storage systems — object stores, frame-indexed access patterns, tiered storage strategies.
• Content-aware processing: Experience with scene detection, shot boundary analysis, temporal segmentation, or perceptual quality optimization.
• Production instincts: Incident response, observability for media pipelines, debugging decode failures at scale, handling format edge cases gracefully.

Nice To Haves

• AI/ML integration experience (strongly preferred): Worked with teams consuming video frames for model training/inference. Understands how preprocessing decisions (resolution, frame rate, chunking strategy) impact model quality.
• Made major contributions to FFmpeg, GStreamer, or open-source media projects.
• Deep familiarity with GPU-accelerated video processing (ex. NVDEC/NVENC).
• Experience running media pipelines in constrained environments such as on-prem or edge settings.
• Candidates must be able to travel up to 10% of the time annually to attend conferences, off-site meetings, and other business-related events as required by the role. This role may require participation in on-site interviews and/or completion of in-person onboarding processes.

Responsibilities

• 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.

Benefits

• An open and inclusive culture and work environment.
• Work closely with a collaborative, mission-driven team on cutting-edge AI technology.
• Full health, dental, and vision benefits
• Extremely flexible PTO and parental leave policy. Office closed the week of Christmas and New Years.
• VISA support where applicable

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed