Research Engineer, AI Models

The role

You will push the boundaries of AI model capability, quality, and efficiency. You will build fine-tuning and post training pipelines, develop benchmarking frameworks, and work on hardware-aware optimization.

What you will do

• Research and implement techniques to accelerate AI inference
• Characterize tradeoffs between model quality, latency, throughput, and power consumption
• Partner with hardware, compiler, and quantization teams
• Identify optimizations aligned with architecture strengths
• Build profiling tools and benchmarking frameworks
• Build robust fine-tuning workflows for modern AI models
• Evaluate new architectures and implement promising techniques

Required qualifications

• 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, state space models etc.
• Experience fine-tuning large models and building training/evaluation pipelines
• Deep understanding of transformers, attention mechanisms, & optimization techniques
• Comfort reading and implementing techniques from research papers

Nice to have

• Experience with efficient inference techniques (KV cache optimization, attention variants, MoE routing, flow matching)
• Background in hardware-aware ML optimization or quantization
• Familiarity with profiling tools (PyTorch Profiler, Nsight, custom instrumentation)
• 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. Their in-memory-computing architecture delivers improvements in compute energy efficiency and performance for AI inference workloads.