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