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
* MSc or PhD in Machine Learning, Computer Science, Applied Math, or related field
* Strong Python engineering fundamentals
* Deep experience with PyTorch, JAX, or TensorFlow
* Hands‑on experience scaling ML systems in production environments
* Familiarity with MLOps tools (Weights & Biases, Ray, Docker, etc.)
* Experience with modern architectures: Transformers, Diffusion Models, SSMs
* Strong sense of ownership and comfort working in fast-paced early-stage environments
Nice-to-Haves
* Contributions to open-source ML tooling
* Experience with distributed training, model compression, or high-throughput serving
* Experience building or integrating ML systems into end-user applications
* Background in scientific computing, biotech, or computational biology (not required)