<h3>About the Role</h3><p style="min-height:1.5em">This is a founding-level, hands-on ML engineering role at a fast-growing <strong>Series A AI startup</strong> in the Bay Area, specializing in high-quality training data, post-training data pipelines, and model evaluation for frontier AI labs and enterprises. You'll work closely with leadership to design, train, and ship production-grade ML systems — while helping shape the technical culture and infrastructure from the ground up.</p><p style="min-height:1.5em"><strong>This role is fully on-site in Mountain View, CA. Candidates must be authorized to work in the US without visa sponsorship.</strong></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 to production deployment.</p></li><li><p style="min-height:1.5em">Implement and fine-tune LLMs, embeddings, and generative models for real-world applications.</p></li><li><p style="min-height:1.5em">Develop efficient training and inference systems leveraging distributed compute at scale.</p></li><li><p style="min-height:1.5em">Partner with data and product teams to translate ideas into measurable ML impact.</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 in model versioning, reproducibility, and scalability across the organization.</p></li></ul><h3>What We're Looking For</h3><p style="min-height:1.5em"><strong>Required</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">3–10 years of hands-on experience as an ML Engineer, Applied Scientist, or Research Engineer.</p></li><li><p style="min-height:1.5em">Proficiency in <strong>Python</strong> and at least one of <strong>PyTorch, TensorFlow, or JAX</strong>.</p></li><li><p style="min-height:1.5em">Proven experience building production-grade, end-to-end ML pipelines (data ingestion, training, deployment).</p></li><li><p style="min-height:1.5em">Experience implementing and fine-tuning LLMs, embeddings, and generative models for real-world use cases.</p></li><li><p style="min-height:1.5em">Hands-on experience with distributed training/inference and scalable ML systems.</p></li><li><p style="min-height:1.5em">Familiarity with cloud platforms (<strong>AWS, GCP, or Azure</strong>) and ML tooling such as <strong>MLflow</strong> or <strong>Weights &amp; Biases</strong>.</p></li><li><p style="min-height:1.5em">Strong collaboration skills with cross-functional data and product teams.</p></li><li><p style="min-height:1.5em">Bias for action, ability to work autonomously, and eagerness to build from scratch.</p></li></ul><p style="min-height:1.5em"><strong>Nice to Have</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Experience with RL environments or evaluation frameworks for AI models.</p></li><li><p style="min-height:1.5em">Backgroun