AI Research Scientist

• Design, prototype, and evaluate novel AI models for autonomous, agent-based systems in healthcare workflows.
• Advance state-of-the-art NLU for clinical, operational, and administrative healthcare text.
• Explore and implement new capabilities in reasoning, decision-making, and multi-modal understanding.
• Adapt and fine-tune large language models to handle healthcare-specific language, regulatory requirements, and ethical considerations.
• Create AI pipelines that can process structured and unstructured healthcare data (FHIR, HL7, clinical notes, claims data).
• Contribute to domain-specific model architectures that improve clinical decision-making, revenue cycle management, and patient engagement.
• Serve as the primary technical authority on Agentic and Generative AI within project teams.
• Mentor junior AI scientists and engineers through code reviews, research guidance, and technical workshops.
• Drive internal knowledge-sharing on emerging AI trends, frameworks, and best practices.
• Implement rigorous model evaluation frameworks for accuracy, robustness, and fairness.
• Ensure compliance with healthcare privacy and data security regulations (HIPAA, HITRUST).

AI Scientist

• Knowledge Graph Design & Construction: Architect and evolve per-tenant knowledge graph schemas, including entity resolution, temporal modeling, and ontology design tailored to sales execution domains.
• Information Extraction: Architect NLP pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, and event detection.
• Contextual Reasoning & Recommendation: Design reasoning and inference layers over the knowledge graph to power next-best-action suggestions, deal risk scoring, coaching recommendations, and competitive intelligence surfaces.
• Representation Learning: Design and train graph-based models (GNNs, relational embeddings, link prediction) over heterogeneous, multi-relational graph structures to support downstream reasoning and retrieval tasks. Diagnose and address embedding quality issues including cold-start entities, and temporal drift.
• Domain Modeling: Formalize sales execution concepts such as deal stages, buyer engagement patterns, rep behaviors, and account health, into structured representations that ground the platform's AI capabilities. Extract ontology structure. Lead ontology versioning and migration.
• Cross-functional Collaboration: Partner with engineering, product, and data teams to bring models from prototype to production, ensuring reliability and measurable impact at scale.

Staff Applied Scientist – Knowledge Graphs, AI

• Knowledge Graph Design & Construction: Design and implement entity resolution and ontology population within established graph schemas. Write and optimize queries for graph traversal and feature extraction. Own data quality for assigned domains.
• Information Extraction: Build pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, and event detection. Run experiments to compare approaches and improve accuracy metrics.
• Contextual Reasoning & Recommendation: Implement graph traversal logic and feature queries that feed downstream scoring signals. Build and maintain features for deal risk, next-best-action, or coaching recommendation surfaces.
• Representation Learning: Train and evaluate link prediction and node classification models using established graph embedding methods. Implement evaluation pipelines and track model performance over time.
• Domain Modeling: Translate sales concepts, such as deal stages, buyer engagement patterns, rep behaviors, and account health, into graph nodes and relationships under the guidance of senior scientists. Contribute to ontology design and documentation.
• Cross-functional Collaboration: Work with software engineers to deploy models and pipelines into production. Write clean, tested code. Monitor system health and respond to incidents. Participate in code review and design discussions.

Senior Applied Scientist – Knowledge Graphs, AI

• Conduct end-to-end research and engineering on vision-language models, covering training, evaluation, and optimization across the full model development lifecycle.
• Design and implement post-training pipelines including supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback.
• Develop and maintain high-quality multimodal datasets, including data curation, filtering, and balancing for domain-specific tasks.
• Drive model efficiency and deployability, adapting models for resource-constrained environments using compression and optimization techniques.
• Design and implement evaluation frameworks and benchmarks to measure model performance, robustness, and real-world task success.
• Build and scale training workflows across distributed GPU infrastructure.
• Identify and resolve bottlenecks in training pipelines to achieve state-of-the-art model quality on target benchmarks.
• Contribute to and leverage open-source ecosystems including models, datasets, and tooling to accelerate development.
• Stay current with the latest research in multimodal learning and vision-language systems, translating relevant findings into practical improvements.
• Publish research findings in top-tier AI conferences and journals where applicable.

AI Research Engineer – Multi-Modal, Vision

• Lead the full lifecycle of AI applications from data exploration to deployment
• Architect agent-based reasoning systems for clinical workflows
• Ensure models are explainable and compliant with regulations

Principal AI Scientist

• Define and drive the research roadmap for core AI capabilities spanning LLMs, ASR/speech understanding, retrieval-augmented generation, and agentic systems.
• Design, train, fine-tune, and evaluate state-of-the-art models, taking them from prototype to robust, production-grade systems.
• Lead the architecture of scalable training and inference pipelines that operate reliably on real customer-scale data.
• Establish rigorous evaluation frameworks, benchmarks, and quality bars for model performance, safety, and reliability.
• Partner closely with product, platform, and ML engineering teams to ship research into customer-facing features.
• Mentor senior and mid-level engineers, set technical standards, and act as a force multiplier across the AI org.
• Stay ahead of the field, evaluate emerging techniques, and represent Level AI's technical work externally where appropriate.

AI Research Scientist jobs in India

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AI Scientist

Staff Applied Scientist – Knowledge Graphs, AI

Senior Applied Scientist – Knowledge Graphs, AI

AI Research Engineer – Multi-Modal, Vision

Principal AI Scientist

Principal AI Research Engineer