Machine Learning Research Engineer

Nuance Labs•Seattle, WA

6d•Onsite

About The Position

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. You understand 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) Nuance Labs Key Facts $10M seed round backed by Accel, South Park Commons, Lightspeed, and top angels including Synthesia’s former CPO. A world-class team of PhDs from MIT, UW, and Oxford with decades of industry experience at Apple and Meta, advancing real-time avatars from cutting-edge research to products used by millions. In-person collaboration, 5 days a week at Seattle HQ

Requirements

Production ML: Experience deploying ML models to production. You understand 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.

Nice To Haves

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)