Start Date: ASAP

About Us Mundane is a seed-stage robot learning startup founded by Stanford roboticists and builders. We’re deploying a fleet of humanoid robots to perform real-world tasks in commercial environments, collecting the data required to train the next generation of embodied intelligence. We move fast, prototype aggressively, test in the real world, and iterate based on reality—not simulations. You can read more at https://mundane.bot .

About the Role We’re looking for a Co-op Engineer to own the video encoding, decoding, and streaming pipeline for a real-time visual system. This is a highly hands-on, execution-focused role. You’ll design and implement a low-latency, high-efficiency pipeline that transmits visual data from edge devices to remote systems under strict performance constraints. You’ll work across video codecs, GPU acceleration, and networking layers to ensure real-time performance in production environments. You should be equally comfortable working with codecs like H.265 or AV1, optimizing pipelines on embedded hardware, and debugging latency and performance bottlenecks across the full video stack.

What You’ll Do Design and implement real-time video encoding and decoding pipelines optimized for low latency and high visual quality Build streaming systems that efficiently transmit video under strict bandwidth and latency constraints Work with modern codecs (H.265/HEVC, AV1) and tune parameters for performance vs. quality tradeoffs Implement region-based or adaptive compression techniques for selective quality encoding within frames Develop packetization and transport layers using RTP, WebRTC, or custom protocols Collaborate with networking engineers to ensure reliable and efficient data transmission Optimize encoding/decoding performance on embedded platforms (e.g., NVIDIA Jetson) Analyze system performance and improve latency, bandwidth usage, and visual stability Contribute to the design of an end-to-end real-time pipeline from capture → encode → transmit → decode → display

Qualifications Proficiency in C++ and experience building performance-critical, real-time systems Experience with GStreamer for building or modifying media pipelines Experience with GPU compute frameworks (CUDA or equivalent) Familiarity with low-level graphics or compute APIs (Vulkan, OpenCL, or similar) Experience working on projects involving video codecs or media processing Understanding of modern video codecs (H.265/HEVC, AV1, or similar) Strong understanding of real-time system constraints (latency, throughput, synchronization) Comfort working close to hardware and debugging performance across system layers

Bonus Experience Experience building real-time video streaming pipelines Familiarity with FFmpeg or hardware-accelerated encoding frameworks Understanding of streaming protocols (RTP, WebRTC, etc.) Experience optimizing workloads on embedded or edge devices Experience with Python for scripting, testing, or automation

What You’ll Get Ownership over core real-time video infrastructure in deployed robotic systems Direct impact on perception, teleoperation, and visual system performance Tight collaboration with a small, high-caliber, multidisciplinary team Exposure to full-stack video systems across hardware, software, and networking Fast iteration cycles with real-world deployment feedback

Perks: Competitive salary + equity, flexible PTO, coffee, robots, sauna & cold plunge (pending)