Nuancelabs2mo ago

Machine Learning Research Engineer

United States·Seattlemid

Data ScienceOtherMachine Learning EngineerMachine Learning Research EngineerData & AI

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Quick Summary

Key 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.

Requirements Summary

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…

Technical Tools

Data ScienceOtherMachine Learning EngineerMachine Learning Research EngineerData & AI

Responsibilities

~1 min read

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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.

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Optimize Model Performance: Profile and improve model inference for latency and throughput using quantization, pruning, distillation, and architectural refinements to ensure viable unit economics

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Model Acceleration: Apply optimization techniques (TensorRT, ONNX, vLLM) to accelerate multimodal models including video diffusion, LLMs, and speech models

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Design Data Pipelines: Design and implement efficient pipelines for video data ingestion, preprocessing, and training at petabyte scale using tools like Dagster and Ray.

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Evaluate and Iterate: Build evaluation frameworks to measure model quality, establish benchmarks, and guide continuous improvement of model capabilities.

Requirements

~1 min read

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 Have

~1 min read

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)

$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.