Research Engineer, AI Models

Responsibilities

• Research and implement state-of-the-art techniques to accelerate AI inference including quantization, sparsity, and distillation
• Partner with hardware, compiler, and quantization teams to ensure algorithmic improvements translate to silicon gains
• Build profiling tools and comprehensive benchmarking frameworks to measure model quality and efficiency
• Build robust fine-tuning workflows for modern AI models using LoRA, adapters, and full fine-tuning

Requirements

• 5+ years of experience in ML research, applied ML, or ML systems
• Strong fundamentals in Python and PyTorch
• Hands-on experience with transformers, diffusion models, and state space models
• Experience fine-tuning large models and building training/evaluation pipelines
• Deep understanding of transformers, attention mechanisms, and optimization techniques
• Ability to read and implement techniques from research papers

Preferred Qualifications

• Experience with efficient inference techniques like KV cache optimization or MoE routing
• Background in hardware-aware ML optimization or quantization
• Familiarity with profiling tools such as PyTorch Profiler or Nsight
• Publications in generative modeling, efficient inference, or ML systems
• Contributions to open-source ML projects

About the Company

EnCharge AI is building the next generation AI platform. Our novel in-memory-computing architecture delivers a 10x step-function improvement in compute energy efficiency and performance for AI inference workloads.