Principal Applied AI Researcher - Domain- Specific Models

About us 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 highstakes environments demand. Job Description Articul8 AI is seeking a Principal Research Scientist to define how we build, evaluate, and scale domainspecific models as a durable source of competitive advantage. You will lead research across the full model development lifecycle: domain data strategy, continued pretraining, supervised finetuning, posttraining, evaluation methodology, and the strategic decisions that determine where Articul8 can create and sustain model superiority in the market. Responsibilities Set companylevel technical direction for domainspecific model strategydefine how Articul8 builds, evaluates, scales, and sustains model superiority across continued pretraining, finetuning, posttraining, and release quality standards, leveraging massively parallel agentic AI systems to compress strategic exploration cycles from months to days Architect the agentic model development paradigm for the organizationdesign the agentorchestrated research infrastructure (experiment orchestration, data pipeline automation, continuous evaluation, competitive benchmarking) that enables every researcher at Articul8 to operate at a fundamentally higher level of depth, breadth, and velocity than would be possible alone Go deep: push the frontier of domainspecific model sciencelead research on model adaptation methodology, data curation strategies, posttraining methods (preference optimization, reward modeling, reasoning improvement, alignment), and training dynamics, deploying fleets of agentic systems to run exhaustive ablation studies, mixture experiments, and failure analyses in parallel Go broad: shape model strategy across all of Articul8's domains and verticalsdefine how the company identifies, prioritizes, and enters new model domains based on technical feasibility, customer value, and strategic differentiation, using agentdriven competitive intelligence and market analysis to scan the landscape continuously Define evaluation strategy as an agentic disciplineestablish benchmark design, expertgrounded assessment, model failure analysis, and robustness standards, building alwayson agentic evaluation harnesses that compare Articul8 models against leading open and closed alternatives and translate findings into concrete investment decisions in real time Lead crosscutting research initiatives that multiply organizational capabilityensure advances in data perception, retrieval, posttraining, and runtime orchestration strengthen the model layer, orchestrating parallel agentdriven research tracks across pillars so breakthroughs in one area compound across the platform Influence platformlevel decisionsshape model lifecycle management, portfolio strategy, release criteria, and integration architecture, ensuring the platform is designed for humans and agentic systems to coevolve and amplify each other Mentor senior researchers and raise the ceiling on human potentialcoach Staff and Senior researchers on designing agentaugmented research programs, raise the bar on technical judgment and experimental rigor, and shape hiring for researchers who are driven to redefine what's possible Maintain handson research impact at the highest levelsustain a meaningful personal research contribution through technical work, publications, patents, and externally visible output, modeling what it means to be a worldclass researcher who uses massively parallel agentic systems to achieve what was previously impossible 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 LLMbased systems. Model lifecycle mastery: Deep handson experience across the full model development lifecycle continued pretraining, supervised finetuning, posttraining 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 domainexpert grounded assessment, systematic error analysis, robustness under distribution shift, and readiness criteria for highstakes deployment. Training at scale: .