Multimodal AI Model Optimization Research Engineer

Skills

About the Role

Optimize cutting-edge research models for fast, efficient production deployment using sparsification, distillation, and quantization. Define optimization metrics, run experiments, benchmark latency, cost, and quality trade-offs, and work 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 or accelerator fundamentals.
• Strong Python coding skills and reliable research-engineering practices.
• Experience working with large models and datasets in cloud environments.
• Ability to read machine-learning papers, reproduce results, and adapt ideas.
• Clear communication and collaboration skills.

Responsibilities

• Optimize research models for efficient, production-ready deployment using sparsification, distillation, and quantization.
• Own the optimization lifecycle for key models by defining metrics, running experiments, and benchmarking latency, cost, and quality trade-offs.
• Partner with researchers and engineers to turn new ideas into deployable systems.

Benefits

• Flexible work schedules.
• Unlimited PTO.
• Healthcare stipend.
• Gear stipend.
• Relocation support.