Principal AI Engineer

Mission Context

We are building a causal decision engine for high-stakes systems where reasoning must be explicit, interventions must be provable, and outcomes must be accountable.

What You'll Work On

• Design and ship causal-aware AI systems that combine LLMs, structured data, time series, and knowledge graphs.
• Build and deploy agentic reasoning workflows for simulation, intervention analysis, and decision support.
• Architect scalable model pipelines across multimodal data sources, from raw signals to verified insights.
• Push models beyond prediction toward explanation, counterfactual reasoning, and causal proof.
• Collaborate with researchers and engineers to translate theory into operational systems.
• Own system performance, reliability, and accountability in real-world, high-stakes environments.

What We Look For

• Strong track record of shipping production AI systems under real-world constraints.
• Deep understanding of modern machine learning and generative models (LLMs, transformers, representation learning).
• Ability to reason about time, causality, and uncertainty — not just fitting models.
• Experience building systems end-to-end and standing behind their outcomes.
• Strong ownership, systems thinking, and technical leadership.

Bonus

• Experience with causal discovery, do-calculus, counterfactual modeling, or causal inference frameworks.
• Experience deploying AI systems in finance, markets, or other high-stakes domains.

Work Style

• Impact-first: focus on outcomes, not process.
• Ownership-driven: take responsibility for what you ship.
• Low-ego, high-agency: value clear thinking over titles.
• Research-meets-reality: theory matters only when it runs in production.

Why Join Abel

• Work on foundational AI systems where reasoning, explanation, and trust truly matter.
• Shape how intelligent systems make decisions in finance and complex operational domains.
• Collaborate with researchers and engineers pushing the frontier of causal intelligence.
• Build systems that move beyond prediction toward accountable, explainable intelligence.