Principal AI Researcher – Agentic Systems, AI Infrastructure

Responsibilities

• Define and evolve the long-term AI/ML research strategy and technical roadmap for Trase OS in alignment with product and platform direction.
• Lead large-scale experimentation and prototyping efforts requiring significant compute infrastructure, translating frontier AI research into scalable, production-grade systems with measurable impact.
• Drive original research and technical breakthroughs in agentic systems, autonomous execution, multi-agent orchestration, post-training and fine-tuning systems, SLM/LLM-based architectures, and applied AI infrastructure.
• Design how models operate within long-lived execution environments, including agent workflows, tool use, planning, memory systems, reasoning, and human-in-the-loop controls.
• Establish evaluation methodologies and reliability frameworks for autonomous systems, including benchmarking, regression testing, safety, controllability, and production behavior analysis.
• Drive architecture decisions across orchestration, model serving, routing, inference, and infrastructure governance, including latency, reliability, and cost optimization.
• Partner closely with engineering and product teams to operationalize research outcomes into deployable systems and enterprise workflows.
• Build AI systems that operate reliably in regulated and constrained environments, including secure cloud, on-premise, and air-gapped deployments.
• Contribute to the broader AI research community through technical papers, publications, conference participation, architecture proposals, and thought leadership.
• Serve as a senior technical authority and mentor across the organization, influencing technical direction, research rigor, experimentation practices, and best practices across research, engineering, and product teams.

Requirements

• 12-15+ years of experience in machine learning, AI systems, or applied AI research, including experience operating at a Principal, Distinguished, or equivalent technical level.
• Strong research and publication track record, including authored papers, major technical contributions, or active participation in frontier AI research.
• Experience publishing at top-tier conferences or contributing influential open-source, research, or AI infrastructure systems.
• Experience conducting large-scale experimentation requiring significant compute infrastructure, evaluation workflows, and iterative model/system analysis.
• Deep expertise in one or more areas including agentic systems, LLMs and generative AI, multi-agent systems, reasoning systems, reinforcement learning, orchestration infrastructure, AI systems reliability, NLP, multimodal systems, or deep learning.
• Hands‑on experience with agent-based systems, prompt engineering, RAG, RLHF, SLMs, fine-tuning/post‑training techniques, tool integration, memory systems, and human‑in‑the‑loop orchestration.
• Proven experience building, deploying, and operating enterprise‑grade AI systems, including Gen AI, LLM, or agent‑based applications at scale.
• Strong understanding of ML system behavior in production, including reliability, latency, cost tradeoffs, observability, evaluation frameworks, regression testing, and failure modes.
• Strong systems thinking and demonstrated ability to partner cross‑functionally with engineering and product organizations to move research into production systems.
• Strong programming and prototyping skills in Python and modern ML infrastructure stacks, with experience in Java or related systems languages preferred.
• Experience deploying AI/ML systems in regulated, constrained, or enterprise environments, and demonstrated ability to lead technical direction from research through production impact.
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