AI Researcher — Inference Optimization

The role

You will design, evaluate, and deploy high-performance inference systems for large-scale machine learning models. The role involves working at the intersection of model architecture, systems engineering, and hardware-aware optimization.

What you will do

• Research and develop techniques to optimize inference performance for large neural networks.
• Improve latency, throughput, memory efficiency, and cost per inference.
• Design and evaluate model-level optimizations.
• Implement systems-level optimizations.
• Benchmark inference workloads across hardware accelerators.
• Collaborate with engineering teams to deploy optimized inference pipelines.
• Translate research insights into production-ready improvements.

Required qualifications

• Strong background in machine learning, deep learning, or AI systems.
• Hands-on experience optimizing inference for large-scale models.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch).
• Experience with inference tooling (e.g., Triton, TensorRT, vLLM, ONNX Runtime).
• Ability to design experiments and communicate results clearly.

Nice to have

• Experience deploying production inference systems at scale.
• Familiarity with distributed and multi-GPU inference.
• Experience contributing to open-source ML or inference frameworks.
• Authorship or co-authorship of peer-reviewed research papers in machine learning, systems, or related fields.
• Experience working close to hardware (CUDA, ROCm, profiling tools).
• Long-context inference optimization
• Speculative decoding
• KV-cache compression and paging
• Efficient decoding strategies
• Hardware-aware inference design

About the company

featherlessai