r/NvidiaJetson

We begin each call with an opportunity for Jetson engineering hiring managers to have their job postings quickly promoted to the community to help connect our community. This is a great opportunity to find active engineers looking to make a career move.

📅 When: 18th August 2026 at 9:00 am PST

📋 Agenda for this month’s Jetson AI Research Lab Call

💼 Employers/Employees Opportunities: (Jetson AI Research Lab Discord Maintainer):

🌾 Deploying autonomous vehicles in agriculture and beyond from **Michael McGuire**, CTO at EarthSense

Abstract: Autonomous outdoor robots are poised to transform agriculture and many other industries. Developing these systems requires innovation at every level of the stack. Successful systems must deliver on hardware, software, and precise autonomy. Concerns such as safety, price, reliability, and manufacturability must be considered from the start. I'm Michael McGuire, CTO at EarthSense, where we develop and deploy robots in corn, soybeans, grapes, oil palm, solar farms, and beyond. The Jetson computer plays a key role in EarthSense's technology stack, ensuring reliable real-time performance at an affordable cost. I'll talk about the unique challenges presented by outdoor autonomy, the solutions EarthSense has developed, and where Jetson fits into our roadmap. Along the way, I'll introduce the specific robots we've built and what running them in real fields taught us.

🤖 Agentic workflow on Jetson and Jetpack 7.2.1 Updates from **Aditya Sahu**, Technical Marketing Engineer at NVIDIA

Jetson Device Skills are agent skills that enable AI coding agents to understand and operate a live NVIDIA Jetson device. Now we have Video SDK skills in it. Updates on Jetpack 7.2.1 release with Support for Jetson T3000 emulation.

As always, this is an open, collaborative call where Jetson enthusiasts gather to:

Showcase: Share novel projects and real-world applications.

Problem-Solve: Break down technical issues and implementation hurdles.

Brainstorm: Discuss the future of edge AI, computer vision, and agent deployment.

Hire or Be Hired: Whether you are looking to hire or looking to be hired this is the hub from aspiring physical AI or embodied AI start their career journey

Whether you're looking to learn how to get your first agent running on an NVIDIA Jetson or want to show off a complex build, we’d love to have your voice in the conversation.

In September we plan to return to our lightning round format showcases projects by members of the community.

Running YOLOv11 (TensorRT) + centroid tracking on a Jetson Xavier NX (MAX-N, jetson_clocks on) for vehicle counting. Get ~27 FPS with few vehicles on screen, but it drops to 8-11 FPS when many vehicles cross at once.

Since it scales with object count, not a flat number, I suspect it's the per-object tracking/post-processing (Python-side) rather than the TensorRT inference itself.

Tried so far:

• Confirmed nvpmodel MAX-N + jetson_clocks are active
• TensorRT engine already used for inference (not raw PyTorch)
• FPS drop correlates directly with number of tracked objects on screen, not with anything else changing

Is stable 30 FPS realistic on a Xavier NX for detection + tracking + per-object logic at this object density, or should I expect this kind of drop and optimize for no dropped frames instead of a flat FPS target?