• Design and enhance backend infrastructure focusing on scalability, performance, and reliability. • Expertly manage concurrency and parallelism in Go using goroutines, channels, and synchronization primitives like mutexes and wait groups. • Develop robust networking and streaming capabilities, including experience with network programming, sockets, and protocols like WebSockets and WebRTC. • Lead performance optimization initiatives by profiling Go code, reducing latency, and efficiently managing memory and garbage collection. • Build fault-tolerant systems with strong recovery mechanisms and failover strategies to maintain service continuity. • Implement comprehensive logging and tracing using tools such as zap, klog, OpenTelemetry, and Jaeger to enhance monitoring and troubleshooting. • Apply Test-Driven Development (TDD) and engage in Pair Programming to ensure high code quality and promote team collaboration. • Participate actively in code reviews, offering constructive feedback to uphold code quality and adhere to best practices. • Stay informed on the latest in software development and contribute to the team's continuous learning and improvement.

• 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.

• 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

• Engage with clients to understand their problems, needs, goals, and technical environments. • Architect, build, and ship full stack applications that leverage AI and data capabilities. • Apply best practices in MLOps or LLMOps, including testing, monitoring, and reliability. • Design and build scalable data pipelines and data lakes as needed. • Communicate tradeoffs and risks transparently, supporting client decision-making. • Collaborate with other engineers and cross-functional teams to deliver impactful results. • Stay current with the latest AI trends, technologies, and tooling to bring innovation to your projects.