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Articul8
Dublin, California, United States
Source: Articul8 careers · View original posting
From Articul8's posting. “We” and “our” refer to the employer.
Articul8 was born from a simple belief: GenAI should work for the enterprise, not the other way around. Our platform — combining domain-specific models, autonomous agentic reasoning (ModelMesh™), reliable model evaluation (LLM-IQ™), and multimodal understanding — serves regulated industries such energy, semiconductor, finance, aerospace, supply chain, and more.
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.
Articul8 AI is seeking a Senior Applied AI Researcher to solve open research problems across our domain-specific GenAI platform. You will own research projects end-to-end — from problem formulation through production deployment. This role spans model training, reinforcement learning, multimodal understanding, and knowledge representation — with deep expertise in at least one area.
PhD or MSc in Computer Science, Machine Learning, or a related field.
5+ years as an AI/ML researcher with shipped research artifacts (models, systems, or tools in production), including 2+ years building LLM-based systems.
You have run multi-stage training pipelines (pretraining, fine-tuning, post-training) and can diagnose training failures from loss curves, gradient norms, and evaluation metrics — not just restart the job.
Deep expertise in at least one of: domain-specific model adaptation, multimodal learning, reinforcement learning from human feedback, knowledge-grounded generation, or retrieval-augmented systems. You've published or shipped production work in your area.
Hands-on experience with distributed training at scale (DeepSpeed, FSDP, Megatron-LM, or equivalent). You understand data parallelism vs. model parallelism and know when each matters.
Production-grade Python, clean abstractions, tested code. You build tools others depend on.
You stay open to being wrong — even on problems you've studied for years. You actively seek perspectives that challenge your assumptions and create space for junior researchers to teach you something new.
You own the outcome end-to-end, from research question to production impact. You know the difference between interesting work and important work, and you choose the latter. Dates and scope are commitments, not suggestions.
You treat your team's problems as your own. You give honest, specific feedback because you care about the person's growth, not just the project. When something is broken, you fix it or flag it — you never walk past it.
You pursue research directions others have written off. You use resource constraints as forcing functions for creative solutions, not excuses. Your ambition is calibrated to what the problem demands, not what feels safe.
You hold yourself to the standard that your work should make the enterprise smarter, not just the model better. You mentor others because raising the bar for the whole team is how you multiply impact.
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