Multimodal AI Model Optimization Research Engineer

Mission

Take cutting-edge research models and make them fast, efficient, and production-ready using sparsification, distillation, and quantization. Own the optimization lifecycle for key models: define metrics, run experiments, and benchmark trade-offs across latency, cost, and quality. Partner closely with researchers and engineers to turn new ideas into deployable systems.

Requirements

• Strong experience in deep learning using PyTorch
• Hands-on experience with model optimization and compression, including knowledge distillation, pruning/sparsification, quantization, and mixed precision
• Understanding of efficient architectures such as low-rank adapters
• Strong understanding of inference performance and GPU/accelerator fundamentals
• Strong Python coding skills and reliable research engineering practices
• Experience working with large models and datasets in cloud environments
• Ability to read ML papers, reproduce results, and adapt ideas
• Clear communication and collaboration skills

Preferred

• Optimization of diffusion models, video/audio generative models, or large language models
• Experience with real-time or streaming systems (low-latency APIs, WebRTC, streaming TTS/video)
• Familiarity with TensorRT, ONNX Runtime, TVM, Triton, or XLA
• Experience writing custom Triton/CUDA kernels or low-level performance tuning
• Experience with experiment tracking, benchmarking, and profiling at scale
• Prior experience in research engineering or applied science roles