ML Infrastructure Engineer, New Grad

About The Role

As a new grad ML Infrastructure Engineer, you'll write production Python for our inference serving stack, including our vLLM integration and public APIs. You'll help bring features like speculative decoding, prompt caching, and tool calling into production on the RDU, and add accuracy and regression tests that catch problems before customers do.

Key Responsibilities

• Implement inference features on the RDU such as speculative decoding, constrained decoding, tool calling, prompt caching, and long-context inference
• Adapt vLLM and related serving frameworks to the RDU architecture
• Build and maintain endpoints in the public inference API
• Write accuracy verification and regression tests for inference features
• Debug latency, throughput, and reliability issues in production services

Requirements & Tech Stack

• Strong programming skills in Python
• Understanding of algorithms, data structures, concurrency, and systems design
• Coursework or project experience in distributed systems or machine learning
• Experience with LLM serving frameworks such as vLLM, SGLang, or TensorRT-LLM
• Experience with inference techniques such as speculative decoding, KV caching, or continuous batching
• Experience with PyTorch

Qualifications

• B.S. in Computer Science, Electrical Engineering, or related field

Preferred Qualifications

• M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field
• Experience building or maintaining APIs or backend services
• Internship or research experience in ML systems or backend infrastructure
• Experience with disaggregated systems - prefill/decode
• Open source project ownership or contributions