Principal Applied AI Researcher - Domain- Specific Models (India)

About the role

Articul8 was born from a simple belief: GenAI should work for the enterprise, not the other way around. Our platform combines domain-specific models, autonomous agentic reasoning through ModelMesh(TM), reliable model evaluation through LLM-IQ(TM), and multimodal understanding to serve regulated industries including energy, semiconductor, finance, aerospace, and supply chain. Trusted by Fortune 500 enterprises, we bring together research, engineering, product, and domain expertise to deliver AI that meets the accuracy, explainability, and auditability standards that high-stakes environments demand. Job Description: Articul8 AI is seeking a Principal Research Scientist to define how we build, evaluate, and scale domain-specific models as a durable source of competitive advantage. You will lead research across the full model development lifecycle: domain data strategy, continued pre-training, supervised fine-tuning, post-training, evaluation methodology, and the strategic decisions that determine where Articul8 can create and sustain model superiority in the market.

Responsibilities

• Influence platform-level decisions - shape model lifecycle management, portfolio strategy, release criteria, and integration architecture, ensuring the platform is designed for humans and agentic systems to co-evolve and amplify each other
• Mentor senior researchers and raise the ceiling on human potential - coach Staff and Senior researchers on designing agent-augmented research programs, raise the bar on technical judgment and experimental rigor, and shape hiring for researchers who are driven to redefine what's possible
• Required Qualifications:
• Education: PhD or MSc in Computer Science, Machine Learning, NLP, or a related field.
• Experience: 10+ years in AI/ML research with an exceptional track record of impact - models or systems you built are in production and measurably changed outcomes. 4+ years developing LLM-based systems.
• Model lifecycle mastery: Deep hands-on experience across the full model development lifecycle - continued pretraining, supervised fine-tuning, post-training alignment, and production evaluation. You've made the hard calls about when a model is ready to ship and when it isn't.
• Evaluation rigor: You have designed evaluation methodology that goes beyond leaderboard metrics - domain-expert grounded assessment, systematic error analysis, robustness under distribution shift, and readiness criteria for high-stakes deployment.
• Training at scale: Direct experience training or adapting model

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