Machine Learning Engineer

Job DescriptionA 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 DoBuild and optimize training & inference pipelines for large models (Transformers, SSMs, Diffusion, etc.)Scale workloads across multi-GPU and distributed systemsOptimize model performance, latency, memory usage, and throughputProductionize research code into robust, repeatable systemsWork closely with researchers to convert exploratory notebooks into production frameworksOwn ML infrastructure components - data loading, distributed compute, experiment trackingDesign modular, reusable ML components used across the engineering orgRequirementsMSc or PhD in Machine Learning, Computer Science, Applied Math, or related fieldStrong Python engineering fundamentalsDeep experience with PyTorch, JAX, or TensorFlowHands-on experience scaling ML systems in production environmentsFamiliarity with MLOps tools (Weights & Biases, Ray, Docker, etc.)Experience with modern architectures: Transformers, Diffusion Models, SSMsStrong sense of ownership and comfort working in fast-paced early-stage environments Nice-to-HavesContributions to open-source ML toolingExperience with distributed training, model compression, or high-throughput servingExperience building or integrating ML systems into end-user applicationsBackground in scientific computing, biotech, or computational biology (not required)