Senior Systems Engineer (ML)

Responsibilities:

• Architect and scale a next-generation multimodal inference platform powering multiple production AI pipelines.
• Building GPU-resident multimodal pipelines for image, audio, and video inference.
• Optimizing PyTorch models with TensorRT and Torch-TensorRT.
• Designing dynamic batching and low-latency inference systems.
• Building GPU-native preprocessing pipelines using NVIDIA DALI and Kornia.
• Supporting inference across heterogeneous GPU fleets (H100 A100 A10G, RTX 4090 etc. ).
• Improving observability, throughput, reliability, and deployment automation.

Requirements:

• Strong Python and PyTorch skills.
• Experience deploying ML models in production.
• Understanding of GPU inference optimization and CUDA fundamentals.
• Familiarity with Docker and Linux.
• Strong debugging and problem-solving skills.
• Experience with NVIDIA Triton, TensorRT, or DALI.
• Familiarity with CUDA profiling and performance optimization.
• Experience with distributed inference systems.
• Knowledge of audio/video ML pipelines.
• Experience with ONNX, AWS GPU infrastructure, and model quantization techniques (FP16/INT8).