Distributed Training Kochi

Overview

Join a highly advanced AI infrastructure team focused on building and optimizing large-scale machine learning systems. This setting leverages cutting-edge technologies to enable high-performance experimentation, scalable model deployment, and productive processing of large datasets.

The team operates globally, bringing together engineers and researchers to push the boundaries of deep learning, distributed systems, and next-generation compute platforms.

About the Role

This position is centered on maximizing the efficiency and scalability of GPU-based machine learning workloads, particularly for large language models (LLMs) and generative AI systems.

You will work on improving both training performance and inference efficiency, ensuring optimal utilization of hardware resources, reduced latency, and faster model iteration cycles. The role requires hands-on expertise in deep learning frameworks, distributed systems, and performance optimization.

Key Responsibilities

Enhance performance of distributed training frameworks such as PyTorch, DeepSpeed, or similar systems

Identify and resolve bottlenecks in large-scale training pipelines (e.g., memory usage, communication overhead, GPU utilization)

Optimize inference systems using techniques like quantization, caching, and batching to achieve low latency and high throughput

Collaborate with infrastructure and platform teams to improve resource orchestration, scheduling, and system reliability

Design benchmarking tools and metrics to measure training efficiency, system throughput, and latency performance

Apply advanced optimization techniques (e.g., mixture-of-experts, speculative decoding, model parallelism) to improve large model performance

Continuously evaluate new approaches to hardware acceleration and model execution efficiency

Required Qualifications

3+ years of hands-on experience optimizing GPU-based machine learning workloads

Solid expertise in deep learning frameworks such as PyTorc Apply on Kit Job: kitjob.in/job/4ncql3