Teleop Networking Engineer, Real-Time Systems & Latency

Our operators control robots across countries and oceans, and the link between them is the product. You'll own that link end to end: getting multiple camera streams to the operator and commands back to the robot with as little delay, jitter, and loss as physics allows, over networks we don't control. You'll set the latency budget for the whole company, measure it continuously, and keep cutting it.

• Design and build the real-time transport between robot sites and operator stations: low-latency video (multiple cameras, possibly depth), robot state, and control commands over the public internet.
• Own the end-to-end latency budget. Instrument glass-to-glass and command-to-motion latency, find where every millisecond goes, and cut it: capture and encode settings, transport and protocol choice, jitter buffers, routing, and decode/render.
• Make the link robust under real conditions: NAT and firewall traversal, packet loss, congestion control, bandwidth adaptation, reconnection, and graceful degradation when quality drops.
• Design the network architecture for a global fleet: relay and server placement, region selection, private backbones vs. public internet, and measuring real paths between operator countries and robot sites.
• Prioritize traffic correctly: control commands and safety signals ahead of video, video ahead of bulk data upload, so recording never competes with piloting.
• Specify and validate site and operator network requirements (bandwidth, latency, jitter, wired vs. wireless, cellular backup) and work with customers' IT teams to meet them.
• Build monitoring and alerting for link health across the fleet, and expose clear connection-quality signals to operators and to the robot's safety layer.
• Partner with the Robotics Software Engineer on the command path into the robot and on time sync, and with the Data & Infrastructure Engineer on moving recorded data off-site without hurting live sessions.

• 3+ years building production level real-time networked systems: video streaming, video conferencing, cloud gaming, remote desktop, live broadcast, or similar.
• Deep, hands-on experience with low-latency media and data transport (e.g., WebRTC, RTP/SRTP, QUIC or custom UDP protocols) and the trade-offs between them.
• Strong understanding of video encoding trade-offs for latency: codec choice, GOP structure, bitrate control, hardware encode/decode.
• You've measured and reduced latency in a production system and can explain exactly where the time went.
• Solid networking fundamentals: TCP/UDP behavior, congestion control, NAT traversal, routing, and reading packet captures.
• Strong in at least one of C++, Rust, Go, or Python; comfortable deploying and debugging on remote Linux machines you can't physically touch.

• Experience with robotics, teleoperation, drones, or other remote-control systems where latency affects safety.
• Built or operated globally distributed relay/TURN infrastructure, or worked on network paths between continents.
• Edge video pipelines on embedded hardware; multi-camera capture and synchronization.
• Cellular, 5G, or bonded-link networking for sites with unreliable connectivity.
• Enough front-end skill to build a simple operator-facing stats or debugging view yourself.

If you don't check every box above but believe you can do the job, we encourage you to apply — we're looking for capability and trajectory, not a perfect checklist match.

We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, we will partner with you to meet your needs.

We may use AI-assisted tools to help evaluate applications against the requirements of this role. These tools support our recruiting team in identifying qualified candidates but do not replace human judgment or limit which applications may be reviewed or considered.

Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.