AI Engineer

REALTEK SINGAPORE PRIVATE LIMITED; Singapore; On-site; SGD 120,000 - 210,000; Full time

Job summary

Realtek's Ameba AIoT product line focuses on Wi-Fi/BT MCU and edge-AI SoCs for smart homes and consumer devices. We seek an AI Engineer to advance AI capabilities across the Ameba software stack, including tooling, vision and speech on edge devices.

You will extend the MCP toolchain for deployment, quantization and validation on Ameba hardware, and collaborate with firmware and platform teams. This role sits at the intersection of applied AI and real hardware.

Qualifications

• Master's degree and 5+ years AI/ML engineering experience.
• Experience building end-to-end ML systems from training to deployed inference.
• Experience building pipelines, APIs or internal platforms used by engineers.
• Practical experience with model optimization and deployment tools (ONNX/OpenVINO/TensorRT).

Responsibilities

• Extend MCP toolchain for model deployment, inference control and NPU reporting.
• Design end-to-end evaluation of AI development workflow across Ameba SoCs.
• Port, quantize and optimize vision and speech models for Ameba NPU platforms.
• Build end-to-end path for customers to bring their own trained models to Ameba.

Skills

Python

Deep Learning

Edge AI

Team Leadership

Education

Master's degree in Electrical Engineering, Electronic Engineering or Computer Science

Tools

ONNX

OpenVINO

TensorRT

Job description

General Summary

Realtek's Ameba AIoT product line delivers Wi-Fi/BT MCU and edge-AI SoCs used in smart home, security, and connected consumer devices worldwide.

We are looking for an AI Engineer to advance AI capabilities across the Ameba software stack — from AI-assisted development tooling that lets developers build on Ameba through natural language, to vision and speech applications that run on our NPU-enabled SoCs.

You will extend our Model Context Protocol (MCP) toolchain so that AI agents can build, deploy, and validate directly on Ameba hardware; bring vision and speech models onto our edge-AI platforms; and turn the results into reference designs that customers and ecosystem partners can adopt.

This is a role for someone who works at the intersection of applied AI and real hardware — comfortable moving a model from training framework, through quantization, onto a memory-constrained device, and then proving with data that it works. You will collaborate closely with our firmware and platform software teams, who own the RTOS, drivers, and low-level platform integration.

Key Responsibilities

• AI Development Toolchain. Extend the Ameba SDK Development MCP server with new agent-facing capabilities — model deployment, inference control, camera capture, and NPU performance reporting — so that AI coding agents can drive the full development loop on Ameba hardware.


• Benchmarking and Quality Measurement. Design and run end-to-end evaluation of the AI-assisted development workflow: task success rate, iteration count, and failure-mode analysis across multiple Ameba SoCs and AI clients. Use the results to guide engineering priorities and improve developer experience.


• Edge AI Model Enablement. Port, quantize, and optimize vision and speech models for Ameba NPU platforms. Produce and maintain a benchmarked model zoo covering inference latency, memory footprint, power consumption, and post-quantization accuracy.


• Bring-Your-Own-Model Workflow. Build and document the end-to-end path for customers to bring their own trained models onto Ameba — conversion, INT8 quantization, accuracy recovery, and on-device validation — including troubleshooting guides for common failure modes.


• Vision Reference Designs. Develop application-level reference designs on Ameba NPU SoCs for scenarios such as human and object detection and commercial video surveillance, validated against real-world performance targets.


• Speech and Voice Applications. Prototype hybrid on-device / cloud voice architectures — on-device wake word and audio front-end, cloud ASR, LLM, and TTS — and characterize the end-to-end latency budget that determines user experience. Benchmark speech technology components against measured accuracy, memory, and power targets.


• Partner and Customer Enablement. Serve as the technical counterpart for international AI/ISV ecosystem partners and support customer teams in adopting Ameba AI capabilities, including reference code, documentation, and issue resolution.

Minimum Qualification

• Master's degree in Electrical Engineering, Electronic Engineering, Computer Science, or a related field, with 5+ years of relevant AI/ML engineering experience.


• Demonstrated end-to-end ownership of machine learning systems in production — from model training and fine-tuning, through optimization and quantization, to deployed inference serving live business operations with measurable outcomes. Research-only or proof-of-concept-only experience does not meet this requirement.


• Experience building and maintaining the pipelines, APIs, or internal platforms that other engineers or end users depend on — not solely notebook-based model development.


• Practical experience with model optimization and deployment toolchains such as ONNX, OpenVINO, or TensorRT, including quantization of models for inference on constrained hardware and systematic recovery of post-quantization accuracy.


• Strong proficiency in Python for model development, tooling, and service implementation.


• Hands-on experience with deep learning frameworks (PyTorch, TensorFlow) for training and fine-tuning models.


• Experience leading technical delivery, either owning a project end to end or directing a small team through a full delivery cycle.


• Ability to read and interpret C source code well enough to understand SDK examples, trace data flow, and communicate precisely with firmware engineers. (Writing production firmware is not required for this role.)


• Professional working proficiency in English, both written and verbal.

Preferred Qualifications

• Experience building developer-facing tools or SDKs consumed by external developers.


• Experience deploying models to embedded, MCU-class, or NPU-accelerated hardware.


• Experience with computer vision model development and deployment (OpenCV, YOLO-family object detection, anomaly detection).


• Experience with speech and audio AI: ASR model fine-tuning, text-to-speech synthesis, or conversational systems.


• Experience building LLM-based applications: retrieval-augmented generation (RAG), agent frameworks, and systematic evaluation of AI system quality.


• Familiarity with the Model Context Protocol (MCP) or comparable AI agent tooling standards.


• Experience delivering technical enablement to external customers or partners.


• Track record of open-source contribution, technical publication, or competitive AI/ML achievements.