I am a Software/Firmware Engineer with 14+ years of experience bridging the gap between hardware constraints and user experience. I specialize in system optimization, embedded systems (bare-metal to Linux), and scalable backends.
- Recent impact: Owned the full audio pipeline at Limitless AI (from firmware audio codec to backend storage) and optimized audio playback latency to <800 ms at p99. At R-Zero, reduced the firmware flashing step by 10 minutes per unit on the manufacturing line.
- Experience: Limitless AI (Wearable), SunPower (Energy), and R-Zero (IoT Health).
Comfortable owning the stack end-to-end—from low-level drivers and real-time firmware to backend ingestion, cloud services, and operations. Strong recommendations available from leads at my last 3 projects.
Open to consulting or full-time. Happy to hop on a call to review your architecture or discuss optimization bottlenecks.
It’s designed for exactly this kind of use—hands-free, continuous voice capture while you go about your day. You get access to your raw data through an open API, so there’s no lock-in.
RT Linux is not the only alternative. There are now plenty of options for achieving hard real-time performance without relying on proprietary RTOS solutions:
Dedicated Real-Time Cores: Many SoCs include dedicated cores specifically for hard real-time tasks.
Microcontroller Companions: Pairing a microcontroller running a lightweight RTOS with a larger, general-purpose processor as a supervisor (You can use normal Linux).
FPGAs: Custom FPGA implementations can deliver deterministic timing where ultra-low latency or specialized processing is needed.
And ofc, RT Linux, that in my experience is suitable for over 80% of hard real-time applications (an estimation, based on experience as a former user—your mileage may vary).
If by "modern" you mean a desktop/server CPU, the problem is the complexity that exists because of optimization for average throughput. Do you really know how long you will wait, worst-case, if three different cores flush the same cache line back to DRAM? Or maybe some niche hardware you use has some funky behaviour that stalls some hardware bus, or waits in the kernel for a millisecond, twice a month or whatever.
On the other hand, on FPGAs, deterministic timing is so very simple. Your output will not be a single clock cycle late even if something else goes wrong in logic running on the same FPGA (except through a connection that you control).
If you really want to know, OSADL has a QA Farm that monitors worst-case interrupt and scheduling latency for various CPUs and Linux versions.
Back in university, I had a course on reactive systems, a whole semester of state machines and statecharts. I can't stress enough how useful it was in my career.
I've retrofitted some near unmaintainable embedded systems, and one of the easiest ways to improve spaghetti code is refactoring some of the vars into state machines/statecharts.
Btw, state machines play well with event sourcing and clustering.
Remote: Yes (US time zones preferred)
Willing to relocate: No
Technologies: C/C++, Embedded Linux (Yocto, RT), Zephyr, Qt, Python, Typescript, AWS, Elixir/Phoenix
Résumé/CV: https://www.linkedin.com/in/jonas-dourado/
Email: jonas.jonaias (at) gmail.com
I am a Software/Firmware Engineer with 14+ years of experience bridging the gap between hardware constraints and user experience. I specialize in system optimization, embedded systems (bare-metal to Linux), and scalable backends.
Highlights:
- Firmware: Yocto, Zephyr, Linux, FreeRTOS, BLE, DSP, ARM/MIPS/x86.
- Backend/Infra: AWS (CloudFormation, IoT), Elixir/Phoenix, CI/CD pipelines.
- Recent impact: Owned the full audio pipeline at Limitless AI (from firmware audio codec to backend storage) and optimized audio playback latency to <800 ms at p99. At R-Zero, reduced the firmware flashing step by 10 minutes per unit on the manufacturing line.
- Experience: Limitless AI (Wearable), SunPower (Energy), and R-Zero (IoT Health).
Comfortable owning the stack end-to-end—from low-level drivers and real-time firmware to backend ingestion, cloud services, and operations. Strong recommendations available from leads at my last 3 projects.
Open to consulting or full-time. Happy to hop on a call to review your architecture or discuss optimization bottlenecks.
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