Machine Learning Engineer

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

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)