SSE - Optimization Engineer

We are looking for a Senior Software Engineer to develop and optimize deep learning models, including CNNs, LLMs, and MoE, for efficient inference across CPU, GPU, hardware accelerators, and edge devices. The role focuses on quantization, model compression, high-performance kernel implementation, transformer optimization, and production deployment.

Responsibilities:

• Develop and optimize deep learning models (CNNs, LLMs, MoE) for efficient inference across CPU, GPU, hardware accelerators, and edge devices.

• Design and implement quantization algorithms (PTQ, QAT, GPTQ, AWQ) from scratch.

• Apply model compression techniques such as pruning, decomposition, and distillation.

• Implement and optimize quantized kernels (INT8, INT4, FP8) using C++ for high performance.

• Translate research papers into production-ready implementations.

• Optimize latency, throughput, and memory usage for real-world deployment.

• Work on transformer optimization including KV-cache, PEFT (LoRA/QLoRA), and MoE models.

• Profile, benchmark, and debug model performance across different hardware platforms.

Requirements

Education:

• 4+ years of relevant experience.

• Strong programming skills in Python and C++.

• Proven experience in quantization algorithms (PTQ, QAT, GPTQ, AWQ).

• Hands-on experience in pruning, model compression, and inference optimization.

• Experience implementing quantization or optimization techniques from scratch.

• Strong understanding of CNNs, Transformers, and LLM architectures.

• Experience with PyTorch / ONNX and model deployment pipelines.

• Strong problem-solving and performance optimization skills.

Need to have (Can be bridged):

No additional bridged skills were specified.

Good to have (Not essential):

• Experience with MoE architectures and PEFT techniques (LoRA, QLoRA).

• Knowledge of TensorRT, ONNX Runtime, TVM, and MLIR.

• Familiarity with hardware-aware optimization across GPU, NPU, and edge devices.

• Experience in research paper implementation or open-source contributions.

Preferred Qualifications (Optional):

No additional preferred qualifications were specified.