Senior Machine Learning Software Developer

2 weeks ago

Hyderabad, Telangana, India beBeeMachineLearningEngineer Full time ₹ 1,72,80,000 - ₹ 2,59,60,000

Job Opportunity

We are seeking an experienced professional to fill a critical role in our team. The ideal candidate will have expertise in deep learning and machine learning software engineering, with a strong background in optimizing and deploying models on specialized hardware.

Key Responsibilities:

- Model Porting & Deployment: Port and deploy complex deep learning models from various frameworks (e.g., PyTorch, TensorFlow) to proprietary or commercial ML accelerator hardware platforms (e.g., TPUs, NPUs, GPUs).

- Performance Optimization: Analyze and optimize the performance of ML models for target hardware, focusing on latency, throughput, and power consumption.

- Quantization: Lead the efforts in model quantization (e.g., INT8, FP16) to reduce model size and accelerate inference while preserving model accuracy.

- Profiling & Debugging: Utilize profiling tools to identify performance bottlenecks and debug issues in the ML inference pipeline on the accelerator.

- Collaboration: Work closely with the ML research, hardware, and software teams to understand model requirements and hardware capabilities, providing feedback to improve both.

- Tooling & Automation: Develop and maintain tools and scripts to automate the model porting, quantization, and performance testing workflows.

- Research & Innovation: Stay current with the latest trends and research in ML hardware, model compression, and optimization

Qualifications:

- Experience: 8-10 years of professional experience in machine learning engineering, with a focus on model deployment and optimization.

- Technical Skills:

- Deep expertise in deep learning frameworks such as PyTorch and TensorFlow.

- Proven experience in optimizing models for inference on GPUs, NPUs, TPUs, or other specialized accelerators.

- Extensive hands-on experience with model quantization (e.g., Post-Training Quantization, Quantization-Aware Training).

- Strong proficiency in C++ and Python, with experience writing high-performance, low-level code.

- Experience with GPU programming models like CUDA/cuDNN.

- Familiarity with ML inference engines and runtimes (e.g., TensorRT, OpenVINO, TensorFlow Lite).

- Strong understanding of computer architecture principles, including memory hierarchies, SIMD/vectorization, and cache optimization.

- Version Control: Proficient with Git and collaborative development workflows.

- Education: Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field

Preferred Qualifications:

- Experience with hardware-aware model design and co-design.

- Knowledge of compiler technologies for deep learning.

- Contributions to open-source ML optimization projects.

- Experience with real-time or embedded systems.

- Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps best practices.

- Familiarity with CI/CD pipelines and automated testing for ML models.

- Domain knowledge in areas like computer vision, natural language processing, or speech recognition

About This Role

This role is crucial for bridging the gap between theoretical model development and practical, high-performance inference on target platforms. A key focus of this position will be on model quantization and other optimization techniques to maximize efficiency and performance.

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