Edge / Embedded AI Engineer (On-device inference)

Full Description

Job Title: Edge / Embedded AI Engineer (On-device inference) — 1 Position

About the Job

We are looking for an experienced Edge / Embedded AI Engineer to design, optimize and deploy ML models that run reliably and efficiently on-device.

You will bridge research-quality models and production firmware by implementing model compression, hardware-specific acceleration, and robust device integration.

The role suits an engineer who knows both ML model internals (quantization, pruning, distillation) and embedded systems (RTOS, cross-compilation, low-power operation), and who enjoys shipping real-world on-device AI features.

Responsibilities

• Convert, optimize and deploy ML models for on-device inference (vision, audio, sensor fusion, or NLP) using frameworks like TensorFlow Lite, ONNX Runtime, PyTorch Mobile, TensorRT, OpenVINO, Vitis AI, EdgeTPU toolchain, etc.

• Implement quantization (post-training & QAT), pruning, knowledge distillation and other compression techniques to meet tight memory/latency/power budgets.

• Integrate models into embedded firmware and edge platforms (MCUs, Cortex-A, Arm NPU, Coral Edge TPU, NVIDIA Jetson, Qualcomm/MediaTek NPUs) and implement efficient inference pipelines.

• Work with RTOS or lightweight OS stacks (FreeRTOS, Zephyr, Yocto, Embedded Linux) and toolchains for cross-compilation, linking and debugging.

• Build and maintain performance/accuracy testing, CI/CD for models and firmware, automated regression tests and reproducible deployment pipelines.

• Profile and optimize inference (latency, throughput, memory, power) using hardware profilers, trace logs and telemetry; propose hardware/software trade-offs.

• Implement secure model provisioning, encrypted model storage, and OTA model update strategies suitable for edge devices.

• Collaborate with product, firmware, hardware and cloud teams to define requirements, system architecture and end-to-end data flows.

• Document model choices, deployment recipes, performance results and runbooks; participate in code reviews and knowledge sharing.

Qualifications

• BS/MS (or equivalent) in Computer Science, Electrical/Computer Engineering, Robotics, or related field.

• 3+ years professional experience deploying ML to edge/embedded platforms or equivalent product experience.

• Strong hands-on skills in Python for ML workflows and C/C++ for embedded integration.

• Proven experience with at least two of: TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorRT, OpenVINO, EdgeTPU/Coral toolchains, Vitis AI.

• Knowledge of model optimization techniques: quantization (INT8/FP16), pruning, fused ops, operator kernels, and accuracy/performance trade-offs.

• Experience with embedded platforms: ARM Cortex-M/A, NVIDIA Jetson, Coral, Qualcomm/MediaTek NPUs, or similar.

• Familiarity with build systems (CMake, cross-toolchains), debugging via JTAG/SWD, and profiling tools.

• Understanding of systems constraints: memory map, caches, DMA, real-time scheduling, power management and thermal considerations.

• Strong problem-solving, communication and documentation skills.

Preferred

• Experience with TinyML / CMSIS-NN, edge computer vision stacks (OpenCV, GStreamer), or audio/speech on-device inference.

• Experience with hardware accelerators and writing/optimizing custom operator kernels.

• Familiarity with secure model lifecycle, encrypted provisioning and OTA strategies.

• Open-source contributions, published work or a portfolio of deployed edge ML projects.

What We Offer

• Work on impactful on-device AI features in a product-driven environment.

• Access to edge hardware lab (Jetsons, Coral, NPUs, dev kits) and cloud resources for training/CI.

• Collaborative cross-disciplinary team and opportunities for ownership and technical leadership.

• Competitive compensation, flexible working arrangements and support for conferences/training.

How to Apply

Please email the following to **** with subject line "Edge / Embedded AI Engineer (On-device inference)":

• CV (max 2 pages)

• Cover letter (1 page) describing a deployed edge/embedded ML project you led or contributed to (challenges, trade-offs, results).

• Links to repo(s), demos, technical notes or short videos (GitHub, GitLab, Colab, etc.).

• Two professional references (name, role, contact).

Shortlisted candidates will be invited to a technical interview and may be asked to complete a short hands-on or take-home task (model optimization or integration exercise).

We welcome applicants from diverse backgrounds and encourage engineers who bridge ML research and embedded productization to apply.