<h3>About the Role</h3><p style="min-height:1.5em">A well-funded AI/ML platform startup (Series A) based in Mountain View, CA is looking for a <strong>Founding Engineer – Machine Learning</strong> to build and scale core ML systems from the ground up. You'll join a small, high-velocity team working closely with the founders to establish technical culture, reliable ML pipelines, and measurable product impact. This is a rare opportunity to shape the architecture and direction of ML infrastructure at an early stage.</p><h3>What You'll Do</h3><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Build and optimize end-to-end ML pipelines from data ingestion through deployment.</p></li><li><p style="min-height:1.5em">Implement and fine-tune LLMs, embeddings, and generative models for real-world use cases.</p></li><li><p style="min-height:1.5em">Develop efficient training and inference systems leveraging distributed compute.</p></li><li><p style="min-height:1.5em">Partner with data and product teams to translate ideas into measurable ML outcomes.</p></li><li><p style="min-height:1.5em">Contribute to model monitoring, evaluation, and continual learning frameworks.</p></li><li><p style="min-height:1.5em">Establish best practices for model versioning, reproducibility, and scalability.</p></li><li><p style="min-height:1.5em">Move quickly between experimentation and production deployments, balancing research and engineering rigor.</p></li></ul><h3>What We're Looking For</h3><h3>Required:</h3><ul style="min-height:1.5em"><li><p style="min-height:1.5em">3–10 years of experience as an ML Engineer, Applied Scientist, or Research Engineer.</p></li><li><p style="min-height:1.5em">Strong ML fundamentals: data preprocessing, feature engineering, model training, and optimization.</p></li><li><p style="min-height:1.5em">Proficiency in Python and at least one deep learning framework: PyTorch, TensorFlow, or JAX.</p></li><li><p style="min-height:1.5em">Experience with distributed training/inference and cloud ML infrastructure (AWS, GCP, or Azure).</p></li><li><p style="min-height:1.5em">Hands-on experience building end-to-end ML pipelines from data ingestion to production deployment.</p></li><li><p style="min-height:1.5em">Familiarity with MLOps tooling (e.g., Weights &amp; Biases, MLflow) and model monitoring approaches.</p></li><li><p style="min-height:1.5em">Comfortable working with large datasets and high-throughput systems.</p></li><li><p style="min-height:1.5em">Bias for action, ability to work autonomously, and eagerness to build systems from scratch.</p></li><li><p style="min-height:1.5em">Willingness to work onsite in Mountain View, CA.</p></li></ul><h3>Nice to Have:</h3><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Experience with vector databases and retrieval-augmented generation (RAG) workflows.</p></li><li><p style="min-height:1.5em">Experience with continual learning, model monitoring, and evaluation frameworks.</p></li></ul><h3>Com