Senior AI Engineer
Doctronic · New York City
About this role
## Senior AI Engineer **Location:** New York City or San Francisco (Hybrid Onsite) **Type:** Full Time --- ### Mission Let’s make high-quality healthcare free and accessible to everyone through AI. Doctronic’s AI doctor already handles millions of consultations—our goal is to scale to billions while continuously improving clinical safety and accuracy. --- ### About the Role Doctronic runs a real clinical practice, with patients consulting our AI doctor every day. That creates a dataset no one else has to test ideas against. We’re committed to publishing and open sourcing as we go. You’ll build the reasoning systems, learning methods, retrieval algorithms, and evaluation infrastructure that allow Doctronic’s clinical AI to improve with each iteration and earn greater autonomy over time. This role blends research and engineering—what matters is real experimental/modeling work you can also ship. --- ### What You’ll Build **Agentic Clinical Reasoning** - Design and implement the next generation architecture behind Doctronic’s AI doctor (reasoning, reflection, verification, tool use, routing, uncertainty handling, escalation) - Build systems where specialized agents/models collaborate to make clinical decisions safely, reliably, and efficiently—supporting self-improvement over time **Evaluation and Measurement** - Build the evaluation platform, rubrics, simulations, and experiments to measure performance and detect when benchmark scores reward the wrong behavior - Determine what to improve (reasoning, retrieval, model behavior, data, engineering) and verify whether interventions truly made the system better **Models and Learning** - Apply methods such as fine-tuning, distillation, reinforcement learning, preference optimization, and prompt/system optimization - Build training data, feedback, reward, and experimentation pipelines that turn evaluation results and clinical expertise into system improvements **Search, Retrieval, and Grounding** - Build search, ranking, retrieval, and grounding algorithms connecting clinical reasoning to trusted medical evidence, partner-specific content, and patient context - Improve and evaluate retrieval based on downstream clinical decision impact—not only document relevance metrics **End-to-End Ownership** - Own problems end-to-end: framing → implementation → measurement of results --- ### How We Work - **Builder-first org:** senior engineers take ownership and run with problems - **High autonomy:** move fast with your judgment, and bring in clinical/product/engineering partners early when needed --- ### Who You Are - Research-minded engineer who wants to build intelligent systems and demonstrate effectiveness - Deep experience in at least two of: - Agentic architectures, reasoning systems, tool use - Model evaluation, experimentation, rubric design - Model training (fine-tuning, distillation, RL) - Search, ranking, retrieval, or RAG - Strong ML fundamentals: training data, objectives, metrics, failure analysis, calibration, validation - Comfortable working where ground truth is incomplete and experts disagree (and questioning what “correct” means) - Thinks about business impact and prioritizes accordingly - Strong engineering fundamentals (build real systems, not just notebooks) - Communicates clearly and collaborates across engineering, clinical, and product teams **Typical Qualifications** - Advanced degree in a quantitative/computational/scientific discipline + 3+ years applie
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