---
title: 'Principal Applied AI Researcher - Domain- Specific Models (Dublin, CA) at Articul8'
canonical: 'https://feeny.ai/job/principal-applied-ai-researcher-domain-specific-models-dublin-ca-articul8-dublin-28ps4ypf0bha'
type: 'job'
last_seen: '2026-09-10'
---

# Principal Applied AI Researcher - Domain- Specific Models (Dublin, CA) at Articul8

- **Company:** Articul8
- **Location:** Dublin, Ireland
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-05-05
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/articul8/9139baf5-acde-4237-b2a2-efcfa75f2bbe/application

**Skills:** Python, PyTorch, DeepSpeed, FSDP, Megatron-LM, LLM-based systems, Distributed frameworks, Model evaluation, Supervised fine-tuning, Continued pretraining, Post-training alignment, RLHF, DPO, Reward modeling, Constitutional approaches, Data curation, Mixture design, Quality scoring

> Lead research and strategy for building, evaluating, and scaling domain-specific AI models using agentic AI systems. Define technical direction, architect research infrastructure, and shape model lifecycle management to create competitive advantages in regulated industries.

## Job description

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 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:

- Set company-level technical direction for domain-specific model strategy — define how Articul8 builds, evaluates, scales, and sustains model superiority across continued pre-training, fine-tuning, post-training, 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 organization — design the agent-orchestrated 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 domain-specific model science — lead research on model adaptation methodology, data curation strategies, post-training 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 verticals — define how the company identifies, prioritizes, and enters new model domains based on technical feasibility, customer value, and strategic differentiation, using agent-driven competitive intelligence and market analysis to scan the landscape continuously
- Define evaluation strategy as an agentic discipline — establish benchmark design, expert-grounded assessment, model failure analysis, and robustness standards, building always-on agentic evaluation harnesses that compare Articul8 models against leading open and closed alternatives and translate findings into concrete investment decisions in real time
- Lead cross-cutting research initiatives that multiply organizational capability — ensure advances in data perception, retrieval, post-training, and runtime orchestration strengthen the model layer, orchestrating parallel agent-driven research tracks across pillars so breakthroughs in one area compound across the platform
- 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
- Maintain hands-on research impact at the highest level — sustain a meaningful personal research contribution through technical work, publications, patents, and externally visible output, modeling what it means to be a world-class 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 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 models on large GPU clusters using distributed frameworks (DeepSpeed, FSDP, Megatron-LM). You understand the interplay between data mixture, training compute, and model quality at a level that informs strategic decisions.
- Software engineering: Proficient in Python and PyTorch. You still write code, review code, and go deep when the problem demands it.
- Strategic leadership: You have shaped research direction at the organizational level — defining what bets to make, what to stop, and how to allocate research investment across competing priorities. People follow your direction because your judgment has been proven right.

Preferred Qualifications:

- Experience building domain-specialized models that outperform general-purpose alternatives on specific, measurable tasks — not just fine-tuned checkpoints, but models with genuine domain understanding.
- Hands-on experience with post-training methods (RLHF, DPO, reward modeling, constitutional approaches) applied to real alignment problems, not just benchmark reproduction.
- Deep experience in data curation for model development — deduplication, mixture design, quality scoring — where your data decisions measurably changed model outcomes.
- Track record of designing evaluation frameworks for enterprise or regulated-industry use cases where a wrong answer has real consequences.
- Publication record at top-tier venues with evidence of sustained research leadership and influence on the field.
- Experience taking model research from prototype to production in a commercial setting where customers depend on the output.
- Domain expertise in one or more of: energy, semiconductor, finance, aerospace, or supply chain — you understand the data, the workflows, and why off-the-shelf models fail.

Professional Attributes (Code42):

- Practice Humility: You lead with questions, not answers. You actively seek evidence that contradicts your strategy and revise publicly when warranted. You build an environment where senior researchers feel safe challenging your direction — because that's how the best decisions get made.
- Bias for Outcomes: You measure your impact by whether Articul8's models win in the market, not by the elegance of the research agenda. You make the hard calls about what to stop, what to double down on, and what to defer — and you own the results.
- Care Deeply: You treat the researchers you mentor as whole people, not output functions. You care about the quality of every model that ships under Articul8's name and intervene personally when standards are at risk. You build systems of feedback and recognition that make excellence visible.
- Dare to Do the Impossible & Embrace Scarcity: You define research bets that could change Articul8's competitive position for years. You don't let current scale limit the ambition of the model strategy. When resources are tight, you find the highest-leverage experiments and execute them with precision.
- Build a Better World: You ensure Articul8's model strategy serves not just business value but the industries and people who depend on these models for critical decisions. You hold the organization accountable for building AI that is trustworthy, auditable, and genuinely useful — because that's the only kind worth building.

## About Articul8

## Company Overview
- **One-liner**: Articul8 provides a full-stack domain-specific generative AI platform for enterprises in regulated industries, enabling them to build expert-level AI applications within their own IT environments.
- **Entity Type**: Private (Series B)
- **Headquarters**: Dublin, California, USA
- **Founded**: 2024
- **Founders**: Arun K. Subramaniyan

## Core Business
- **Primary industry/industries**: Enterprise AI, Generative AI, Domain-Specific AI
- **Target customers**: Large enterprises in regulated industries including energy, manufacturing, aerospace, semiconductors, and financial services (B2B, Enterprise)
- **Mission or purpose statement**: To transform enterprise data and expertise into powerful engines of growth, value, and impact by making it straightforward for companies to build sophisticated, enterprise-scale, and expert-level GenAI applications that encode their domain expertise.

## Products & Services
- **Articul8 Platform**: A full-stack GenAI platform that processes enterprise data into AI applications, facilitating decision-making, data automation, and adherence to security and privacy standards. It features autonomous agentic reasoning, model evaluation and dynamic routing (LLM-IQ™), and a proprietary ModelMesh™ for multi-agent collaboration.
- **Domain-Specific Models (DSMs)**:
  - **A8-Energy**: Developed with EPRI; trained on 10,000+ specialized energy datasets for expert reasoning in the energy sector.
  - **A8-SupplyChain**: Optimized for manufacturing and supply chain operations; reasons over complex technical documentation without data replication.
  - **A8-Fin**: Finance-focused DSM for tasks such as tabular analysis, portfolio management, and compliance.
  - **A8-Semicon**: Verilog-capable DSM for semiconductor engineering, integrating domain knowledge with reasoning for complex chip design workflows.
- **Hyper-Personalized Agent Models**: Developing hyper-personalized agents for enterprise users.

## Market Standing
- **Valuation/Market Cap**: $500 million pre-money valuation (Series B, January 2026)
- **Key Metric**: Total funding of $35M raised (Series B, first tranche); $100 million in total contract value; projected annual recurring revenue of just over $57 million for 2026
- **Notable Investors/Partners**: Adara Ventures (lead), Aditya Birla Ventures, Accel, Peak XV Partners, NXC, and 14+ others. Partners include Nvidia, Google Cloud, AWS, and Databricks. Customers include Hitachi Energy, AWS, Franklin Templeton, Intel, AIAA, Itochu Techno-Solutions Corporation, Uptycs, and NIQ.
- **Growth Signals**: Revenue-positive with 29 paying customers; 5x valuation increase from Series A ($100M post-money) to Series B ($500M pre-money); expanding internationally with focus on Europe and Asia (Japan, South Korea, India); recognized by Gartner as a Tech Innovator in Domain-Specific AI for Manufacturing and Energy.

## Competitive Advantages
- **Domain-Specific Focus**: Unlike general-purpose models, Articul8’s DSMs are purpose-built for regulated industries, achieving 90%+ accuracy versus ~60% for competing models in proprietary benchmarks, and 28% more accurate than leading general LLMs.
- **Enterprise-Grade Security**: SOC 2 Type II compliant with certified security and confidentiality standards, observability, auditability, and traceability at every step.
- **Cost Efficiency**: 3.5x cheaper than best open-source models, with a 2x performance boost over the latest open-source state-of-the-art models.
- **Proven Performance**: Matches or exceeds proprietary models like Google Flash 2.0 and GPT-4o at a fraction of the compute cost.
- **Customer Lock-In**: Deep integration into customer IT environments and domain-specific workflows creates high switching costs.

## Strategic Focus
- **International Expansion**: Scaling operations in Europe (backed by Adara Ventures and the European Investment Fund) and Asia (Japan, South Korea, India).
- **Heritage AI Venture**: Raising $30-50 million for an India-based AI entity focused on traditional knowledge systems (Sanskrit, classical Indian languages) with applications in drug discovery, metallurgy, healthcare, and education.
- **Product Development**: Expanding research and product development, particularly in agentic reasoning systems and hyper-personalized models.
- **Marketplace Availability**: Available on AWS, Microsoft Azure, Google Cloud Platform, and Databricks to simplify deployment and integration.

## Why Work Here
- **Culture**: 80% of the 75-person team is focused on R&D, indicating a strong engineering and research culture. The company is revenue-positive and not cash-strapped, providing stability.
- **Remote/Hybrid Policy**: Offers remote and hybrid roles across offices in Dublin, CA (USA), Brazil, and India.
- **Notable Perks**: Opportunity to work on cutting-edge domain-specific AI for regulated industries; involvement in a high-growth spinout from Intel with strong backing from top-tier VCs; chance to contribute to heritage AI initiatives preserving ancient knowledge systems.
- **Engineering Culture**: Emphasis on autonomous decisions and actions, automated data intelligence, and building expert-level GenAI applications. Roles include Applied AI Researchers, Software Engineers, and Infrastructure Engineers.

## Sources
1. [articul8.ai](https://www.articul8.ai/)
2. [cbinsights.com](https://www.cbinsights.com/company/articul8-ai)
3. [techcrunch.com](https://techcrunch.com/2026/01/07/intel-spin-off-articul8-is-halfway-to-70m-ai-funding-round-at-500m-valuation/)
4. [builtin.com](https://builtin.com/company/articul8-ai)
5. [pitchbook.com](https://pitchbook.com/profiles/company/550870-03)

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