Machine Learning Research Engineer

Responsibilities Operationalize Research: Collaborate with researchers to move models from experimental checkpoints to production‑ready systems. Establish patterns for large‑scale training, rapid experimentation, and deployment of new architectures. Optimize Model Performance: Profile and improve model inference for latency and throughput using quantization, pruning, distillation, and architectural refinements to ensure viable unit economics.

Model Acceleration: Apply optimization techniques (TensorRT, ONNX, vLLM) to accelerate multimodal models including video diffusion, LLMs, and speech models. Design Data Pipelines: Design and implement efficient pipelines for video data ingestion, preprocessing, and training at petabyte scale using tools like Dagster and Ray. Evaluate and Iterate: Build evaluation frameworks to measure model quality, establish benchmarks, and guide continuous improvement of model capabilities.

Requirements Production ML: Experience deploying ML models to production. Understanding of common failure modes and how to address them (resource contention, OOMs, batch optimization). Deep Learning Experience: Strong knowledge of PyTorch and modern ML architectures.

Experience training and optimizing large models (transformers, diffusion models, or similar). Systems Proficiency: Comfortable working with GPUs, debugging CUDA issues, and profiling model workloads to identify compute or memory bottlenecks. Data Engineering: Experience building scalable data pipelines for high‑bandwidth media processing and training workflows.

Preferred Experience Experience with video or audio models in research or production settings. Familiarity with low‑level optimization (CUDA kernels, Triton, custom operators). Knowledge of real‑time ML systems and latency‑critical inference.

Prior work with model compression techniques (quantization, distillation, pruning). #J-18808-Ljbffr