1 day ago
Taipei, TaiwanMid Level
Responsibilities
- Evaluate open-weight and commercial models for on-device execution.
- Benchmark model performance on target mobile hardware.
- Integrate models into Android applications and system services.
- Optimize inference latency, memory consumption, and power usage.
- Evaluate quantization, compression, and model distillation techniques.
- Work with CPU, GPU, and NPU acceleration.
- Evaluate local model capabilities for different workflows.
- Build hybrid local/cloud model routing mechanisms.
- Determine which tasks can run locally versus through cloud inference.
- Integrate local models into AI agent runtimes.
Requirements
- Strong ML systems or inference engineering experience.
- Experience deploying and optimizing models.
- Strong Python and C++ skills.
- Understanding of neural network inference.
- Experience with model quantization and performance optimization.
- Understanding of hardware constraints and inference benchmarks.
- Preferred: experience with on-device LLMs.
- Preferred: experience with Qualcomm QNN, MediaTek NeuroPilot, or similar acceleration frameworks.
- Preferred: experience with ONNX Runtime, LiteRT, TensorFlow Lite, or ExecuTorch.
- Preferred: experience with Android NDK, mobile or edge AI deployment, and multimodal model optimization.
Categories
About Bjak
Bjak builds an online insurance marketplace for consumers in Malaysia and Southeast Asia, enabling comparison and purchase of car, health, life, and travel policies. Its business model is aggregator-based, earning commissions and fees from insurers and partners while streamlining digital applications and renewals. Founded in 2019 and headquartered in Selangor, it is privately held and is expanding from insurance comparison into broader fintech services via a planned finance super app.