Sr. AI Researcher

Responsibilities:

• Owned data problem formulation for AI systems using healthcare, claims, coding, and payer data.
• Designed training data strategies, including data extraction, labelling, annotation, and quality improvement.
• Mine large, complex datasets to identify high-value patterns for improving AI/coding accuracy.
• Architect and evolveAI/ML platformscovering training, batch processing, and real-time inference.
• Build multi-modal AI systems using clinical text, structured EHR data, and domain knowledge.
• Evaluate AI tools/platforms and drive build vs. buy decisions.
• Mentor AI engineers across NLP, Traditional ML, and GenAI.
• Collaborate with Data Engineering, GenAI/NLP, and Product teams on data and model strategy.

Requirements:

• 8+ years building and deploying production AI/ML systems.
• Strong experience withcomplex, domain-specific data and high-cardinality datasets.
• Deep expertise across NLP, Traditional ML, and GenAI, with production experience in each.
• Expert-level PyTorch or TensorFlow with experience in model training at scale.
• Strong hands-on experience with data quality, labelling, and training-data curation.
• Experience with AWS/Azure/GCP and containerised AI workloads using Docker/Kubernetes.

Skills

ACL, DPO, EMNLP, ICLR, ICML, LoRA, NAACL, NeurIPS, PEFT, QLoRA, SFT, distillation, pretrained, quantization-aware training, trained from scratch