Design and build distributed training platforms for LLM and multimodal fine-tuning and post-training at scale
Implement and integrate state-of-the-art training algorithms into production pipelines
Own inference architecture and multi-provider routing, including failover and optimization
Research and implement inference optimizations including quantization, speculative decoding, and batching strategies
Lead initiatives to improve latency and cost efficiency across the training and serving stack
Build evaluation and experimentation infrastructure that enables rapid, reliable iteration
Drive technical direction, mentor engineers, and establish best practices for ML infrastructure

Requirements

8+ years building ML infrastructure or production systems at scale
Deep experience with distributed training: multi-node GPU clusters, fault tolerance, and optimization
Strong understanding of LLM inference: latency optimization, provider tradeoffs, and serving architecture
Proficiency in Python and modern ML frameworks (PyTorch, JAX, or TensorFlow)
Proven track record leading complex, multi-quarter technical projects

Benefits

Medical, dental, and vision benefits
Take what you need vacation policy
Daily lunches, dinners and snacks in the office to keep you at your best

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

distributed trainingmulti-node GPU clusterslatency optimizationinference architecturequantizationspeculative decodingbatching strategiesPythonPyTorchTensorFlow

Soft Skills

leadershipmentoringtechnical directionbest practices establishment