SSE - Optimization Engineer

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**Title - Senior Software Engineer

**Job Description

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

- Collaborate with ML, compiler, and hardware teams to deliver optimized solutions.

**Must-Have

- BE/BTech/MS/MTech in Computer Science or related field with 4+ years of 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.

**Nice-to-Have
- Experience with MoE architectures, and PEFT techniques (LoRA, QLoRA).

- Knowledge of TensorRT, ONNX Runtime, TVM, MLIR.

- Familiarity with hardware-aware optimization (GPU, NPU, Edge Devices).

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

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