Principal Machine Learning Engineer

What you'll do

• Architect the technical infrastructure for composing and serving heterogeneous generative model pipelines at enterprise scale.
• Develop and optimize GPU-accelerated inference pipelines to improve latency, throughput, and cost-to-serve.
• Design the product backend and API ecosystem for internal applications and external plugin integrations.
• Provide hands-on technical leadership and mentorship to engineers on architecture, design, and implementation best practices.
• Research and evaluate emerging MLOps technologies, including quantization and GPU scheduling, to improve system performance.
• Set technical standards and lead design reviews to ensure high reliability and maintainability across all systems.

What we're looking for

• MS or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent industry experience.
• 8+ years of experience in machine learning engineering, including production-scale deployment and serving.
• 3+ years leading the technical direction of large-scale, GPU-intensive GenAI inference systems.
• Deep experience with inference frameworks and tools such as PyTorch, CUDA, Triton, TensorRT, Nvidia Dynamo, and Python.
• Strong understanding of generative model architectures including diffusion models, transformers, GANs, and LLMs.
• Proven experience architecting multi-model pipelines and serving them behind APIs at enterprise scale.
• Experience designing product backend systems and plugin architectures for internal and external integrations.
• Proven success leading cross-functional teams through complex technical initiatives in matrixed organizations.