Applied ML Engineer

📋 Description Reproduce and evaluate machine learning research methods using open-weight and API-accessible Design evaluation datasets, probes, scoring approaches, baselines, calibration tests, and Work directly with model weights, logits, hidden states, activations, model APIs, and inference Build and extend evaluation infrastructure covering experiment runners, judges, persistence Turn research workflows into intuitive product experiences, including experiment configuration Investigate how verification methods behave when models are modified through fine-tuning, merging 🎯 Requirements Strong Python engineering skills, with hands-on experience using PyTorch and Hugging Face Solid understanding of machine learning evaluation, including dataset design, baselines, metrics Ability to read ML research papers critically and implement methods from first principles rather Professional software engineering experience beyond notebooks, including APIs, asynchronous jobs Familiarity with open-weight models and a practical understanding of how modern LLM inference Ability to work across backend and frontend boundaries, with sufficient React/TypeScript knowledge 🎁 Benefits Opportunity to work on applied machine learning at the intersection of research, experimentation End-to-end ownership across model evaluation, model internals, infrastructure, backend systems, and Exposure to modern open-weight models, LLM inference systems, and emerging ML verification A role with significant technical autonomy and the opportunity to shape both experiments and Fast-moving startup environment with evolving priorities and cross-functional collaboration. Opportunity to translate cutting-edge research into practical, measurable, and user-accessible