Machine Learning Engineer

2 weeks ago

Hyderabad, Telangana, India IIT Hyderabad Full time US$ 1,50,000 - US$ 2,00,000 per year

Job Summary :

We are seeking a highly experienced and skilled Machine Learning Software Engineer with 8-10 years of experience to join our team. The ideal candidate will be a deep learning expert with a strong background in optimizing and deploying machine learning models on specialized hardware, particularly ML accelerators. This role is critical 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.

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 bottleneck 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 techniques.

Required 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.

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