Job Description
A venture‑backed deep‑tech startup is hiring a Machine Learning Engineer with strong experience in scaling training and inference pipelines for modern foundation models.
You’ll work at the intersection of ML research, infrastructure, and product engineering - turning cutting‑edge model code into scalable, reliable systems used in real‑world applications. This is a high‑ownership role suited for someone who loves distributed systems, multi‑GPU scaling, model optimization, and fast iteration.
What You'll Do
* Build and optimize training & inference pipelines for large models (Transformers, SSMs, Diffusion, etc.)
* Scale workloads across multi‑GPU and distributed systems
* Optimize model performance, latency, memory usage, and throughput
* Productionize research code into robust, repeatable systems
* Work closely with researchers to convert exploratory notebooks into production frameworks
* Own ML infrastructure components — data loading, distributed compute, experiment tracking
* Design modular, reusable ML components used across the engineering org
1. Requirements
2. MSc or PhD in Machine Learning, Computer Science, Applied Math, or related field
3. Strong Python engineering fundamentals