Machine Learning Platform Engineer

Python

PyTorch / JAX

LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM

Cloud infrastructure

Distributed systems

ML/data pipelines and workflow orchestration

GPU infrastructure and performance tooling

Vector databases and retrieval infrastructure

Strong software engineering fundamentals and experience building production systems

Experience building ML infrastructure, platforms, or production machine learning systems

Experience with model deployment, inference, evaluation, or data pipelines

Strong understanding of distributed systems and system reliability

Ability to write clean, maintainable, production-quality code

Comfortable working in ambiguous, fast-moving environments

Bias toward ownership, experimentation, and continuous improvement

AI infrastructure reliably supports production workloads at scale

Models can be trained, evaluated, deployed, and improved efficiently

Inference systems deliver strong latency, throughput, reliability, and cost efficiency

ML pipelines are reproducible, observable, maintainable, and robust

Model and infrastructure regressions are detected quickly and diagnosed efficiently

Common ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI product

The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge