AI Lead Engineer

The core responsibilities for the job include the following:

Model Fine-Tuning (core of the role):

• Fine-tune and adapt ASR models for domain vocabulary, accented and multilingual speech, noisy audio, and multi-speaker consultations.
• Fine-tune LLMs for diagnostic and clinical reasoning, symptom extraction, differential generation, structured summarization, and clinical coding.
• Run and debug real training jobs end-to-end, and diagnose runs that plateau, diverge, or catastrophically forget.
• Decide when fine-tuning is the right answer versus prompting, retrieval, or a change of base model.

Data and Evaluation:

• Own training data: curation, labeling strategy, synthetic generation, augmentation, deduplication, and leakage checks.
• Build an evaluation that predicts production behavior: WER/CER sliced by accent and speaker, entity-level accuracy on clinical terms, hallucination, and omission rates.
• Run clinician-in-the-loop review and convert qualitative complaints into measurable objectives.

Production and Optimization:

• Distillation, quantization, streaming and latency optimization, batching, and cost per hour of audio.
• Partner with platform/MLOps on serving, monitoring, drift detection, and retraining cadence.

Technical Leadership (approx. 20-25% of the role):

• Set applied-AI direction for the team; run design reviews and make build-vs-buy calls.
• Mentor 3-5 engineers on evaluation discipline and experiment hygiene.
• Communicate model capability and limitations honestly to product, clinical, and leadership stakeholders.