---
title: 'Staff Applied AI Researcher - Agentic Reasoning Systems (Dublin, CA) at Articul8'
canonical: 'https://feeny.ai/job/staff-applied-ai-researcher-agentic-reasoning-systems-dublin-ca-articul8-dublin-nzvspw5v8gt3'
type: 'job'
last_seen: '2026-09-10'
---

# Staff Applied AI Researcher - Agentic Reasoning Systems (Dublin, CA) at Articul8

- **Company:** Articul8
- **Location:** Dublin, Ireland
- **Employment:** full-time
- **Posted:** 2026-05-05
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/articul8/db4ed650-680f-46d8-bec3-dee21c5620dc/application

**Skills:** Python, Machine Learning, Artificial Intelligence, Large Language Models, Agentic Reasoning Systems, Multi-agent Coordination, Probabilistic Inference, Model Routing, Software Architecture, LLM Evaluation Frameworks, Production System Design, Bayesian Inference, Control Theory, Formal Verification, Probabilistic Modeling, Dynamic Routing, Verification Loops, Reliability Engineering, Knowledge Graphs, Retrieval Systems

> Lead research on runtime intelligence and autonomous agentic reasoning systems within ModelMesh. Define technical direction for orchestration strategies, decision policies, and evaluation methods to ensure reliable, trustworthy AI behavior for enterprise production.

## 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 Staff Applied AI Researcher to define how our platform reasons at runtime and how autonomous systems make trustworthy decisions in production. You will lead research across the core runtime intelligence capabilities behind ModelMesh(TM): task decomposition, agent coordination, model and tool routing, probabilistic decisioning, verification, observability-aware execution, and the evaluation methods that determine whether autonomous behavior is reliable enough for enterprise use. Responsibilities:

- Set technical direction for agentic reasoning systems and runtime intelligence across ModelMesh™ — define the orchestration strategies, decision policies, verification approaches, and runtime quality standards that determine how massively parallel agent systems reason, coordinate, and self-correct in production
- Architect the infrastructure for researcher augmentation at scale — design the agentic platforms and orchestration primitives that enable every researcher and engineer at Articul8 to deploy fleets of AI agents for experimentation, evaluation, and production integration — multiplying the depth, breadth, and velocity of the entire organization
- Go deep: advance the science of autonomous reasoning — design, train, and refine the learned components behind runtime decisioning (routing models, verification models, confidence estimators, reward models, policy selectors), using massively parallel agent-driven experiment pipelines to explore architectural and algorithmic frontiers exhaustively
- Go broad: unify perception, retrieval, reasoning, and action — build repeatable methodology for composing domain-specific models, data perception systems, knowledge graphs, retrieval layers, and external tools into coherent agentic workflows, delegating integration testing and cross-modal benchmarking to parallel agent systems so you can reason across the full stack simultaneously
- Drive research on agent reliability for regulated environments — lead failure detection, self-checking, verification workflows, compounding error analysis, and auditable autonomous behavior research, using agent-orchestrated stress testing and red-teaming at scales that manual evaluation cannot reach
- Define evaluation methodology for runtime intelligence — establish how task success, decision quality, robustness, traceability, and failure recovery are measured under realistic enterprise conditions, building agentic evaluation harnesses that run continuously and surface regressions before they reach customers
- Influence platform-level architecture — shape decisions on model routing, tool use, observability, governance, access control, and interoperability with external agent ecosystems, ensuring the platform is designed for humans and agents to amplify each other
- Mentor researchers across levels in the agentic paradigm — raise the bar on technical judgment, experimental rigor, and agent-augmented research practice; contribute to hiring researchers who are driven to maximize their human potential
- Maintain hands-on research impact — sustain a meaningful personal research contribution through technical work, publications, patents, and externally visible output, modeling what it looks like to be a deeply technical leader who uses agentic systems to go deeper and faster than ever before

Required Qualifications:

- Education: PhD or MSc in Computer Science, Machine Learning, AI, Robotics, or a related field.
- Experience: 8+ years in AI/ML research with demonstrated impact on production systems, including 3+ years building LLM-based or autonomous AI systems.
- Reasoning and orchestration: Deep hands-on experience in at least two of: multi-agent coordination, planning under uncertainty, sequential decision-making, probabilistic inference, model routing, or tool-using agent systems. You've built systems where multiple models must collaborate to produce a reliable outcome.
- Evaluation of autonomous systems: You have designed evaluation frameworks for systems where correctness is not binary — measuring decision quality, reliability under distribution shift, compounding error rates, and failure recovery in production-like conditions.
- Systems at scale: You have designed and operated research systems that integrate multiple models, data sources, and control mechanisms in production or near-production settings. You understand the difference between a demo and a system.
- Software engineering: Proficient in Python with strong software architecture instincts. Your systems are maintainable, testable, and operable.
- Technical leadership: You have set technical direction for a research area, mentored researchers, and influenced quality standards beyond your immediate team.

Preferred Qualifications:

- Experience building orchestration systems with non-trivial control flow — dynamic routing, verification loops, probabilistic gating — not just prompt chaining or fixed DAGs.
- Background in probabilistic modeling, Bayesian inference, control theory, or formal verification applied to ML systems — you can reason about uncertainty, not just measure it.
- Experience with reliability engineering for autonomous AI in regulated environments: observability, safety constraints, graceful degradation, and audit trails.
- Track record of integrating heterogeneous components (retrieval, knowledge graphs, domain models, external APIs) into systems that are more reliable than their individual parts.
- Strong publication record with evidence of sustained, focused research impact — not just breadth.
- Experience taking reasoning or agent systems from prototype to production serving real enterprise customers.
- Domain familiarity in energy, semiconductor, finance, aerospace, telecom, or supply chain.

Professional Attributes (Code42):

- Practice Humility: You recognize that setting technical direction is a responsibility, not a status. You change your mind publicly when the evidence demands it and build a team culture where the best idea wins regardless of who proposed it.
- Bias for Outcomes: You define success by customer and platform impact, not research novelty alone. You make hard prioritization calls and hold yourself accountable for whether the team's work moved the needle.
- Care Deeply: You take personal responsibility for the reliability and trustworthiness of the systems your team builds. You invest in the people around you — their growth, their clarity, their ability to do their best work — because that's how real quality is sustained.
- Dare to Do the Impossible & Embrace Scarcity: You take on problems that don't have known solutions and structure them into tractable research programs. You don't wait for perfect resources — you build with what you have and make the case for what you need with results, not requests.
- Build a Better World: You ensure that the autonomous systems you build are worthy of the trust enterprises place in them. You hold the team to standards of auditability, reliability, and fairness that go beyond what's required — because you believe the bar should be set by builders, not regulators.

## 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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