AI Researcher — Inference Optimization

📋 Description

• Research and develop advanced techniques to improve inference performance for large neural networks
• Optimize key performance dimensions including latency, throughput, memory efficiency, and cost per
• Design and evaluate model-level optimization techniques such as quantization, pruning, KV-cache
• Implement systems-level optimizations including dynamic batching, kernel fusion, multi-GPU
• Benchmark and profile inference workloads across different hardware accelerators to identify
• Collaborate closely with engineering teams to integrate optimized inference techniques into

🎯 Requirements

• Strong background in machine learning, deep learning, AI systems, or a closely related technical
• Hands-on experience optimizing inference workloads for large-scale machine learning or neural
• Strong proficiency in Python and experience with modern machine learning frameworks such as PyTorch.
• Practical experience with inference and model-serving technologies such as Triton, TensorRT, vLLM
• Ability to design rigorous experiments, interpret performance results, and communicate technical
• Experience deploying production inference systems at scale is highly desirable.

🎁 Benefits

• Full-time opportunity within a research-focused AI environment.
• Fully remote work from India.
• Opportunity to work on large-scale machine learning models and high-performance inference systems.
• Exposure to advanced model optimization, systems engineering, and hardware-aware AI techniques.
• Opportunity to contribute to production systems where research can generate measurable improvements
• Hands-on experience with modern inference technologies and hardware acceleration.