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Description
Hi @veblush,
I’m reaching out after reviewing several of your recent contributions to TFLM, including:
- Your review/merge work on the CMSIS‑NN const‑tensor safety updates (closing Additional const-tensor checks (CMSIS_NN) #3229). 1
- Maintenance of the platform‑porting guidance, including the recently updated “New Platform Support” documentation. 2
- Your involvement in runtime correctness fixes, such as the buffer‑index issue resolved in PR Support for DECODE operator #3162. 3
Who I am:
My name is DeeDee Breaux, and I’m a Talent Acquisition Business Partner at Ambiq, a semiconductor company focused on ultra‑low‑power compute for Edge AI. I’m supporting hiring for an Edge AI ML Engineer role, which is centered on TinyML deployment and optimization on microcontrollers.
Role link: https://job-boards.greenhouse.io/ambiqmicroinc/jobs/4114732009
Why I’m contacting you:
Given your involvement across CMSIS‑NN kernel reviews, platform enablement documentation, and runtime stability fixes, you appear to have exactly the type of expertise we’re seeking. I’d value a brief conversation to explore:
- Whether our Edge AI ML Engineer role aligns with your interests, or
- Whether you might recommend others in the TFLM ecosystem who also work deeply on kernel optimization, memory planning, or platform integration.
Some technical areas our team focuses on:
- Performance tuning in
tensorflow/lite/micro/kernels/cmsis_nn/* - Cross‑platform portability design, allocator behavior, and integration patterns for new MCU/NPU platforms
- Memory planner tradeoffs and interpreter correctness under very tight RAM/Flash constraints
I’m starting the conversation here to stay consistent with TFLM’s guidance that GitHub Issues are the primary contact path for maintainers. 4
If you're open to it, I’d appreciate a short discussion—or any referrals you feel comfortable sharing.
Thank you again for all you do to support TFLM.
— DeeDee Breaux
Talent Acquisition Business Partner, Ambiq