Principal AI Researcher (Agentic Systems & AI Infrastructure)

SLMs (small language models), model optimization, and fine-tuning recipes,

post-training adaptation techniques and model behavior shaping,

and evaluation frameworks for autonomous agents. This role will help define how next-generation AI systems are researched, evaluated, and safely operated in production. ### 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 GenAI, 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. ### Preferred Qualifications

PhD in Computer Science, Machine Learning, AI, Systems, or a related field. - Experience building and operating AI/ML platforms supporting the full model lifecycle, including training, evaluation, deployment, and monitoring. - Experience optimizing ML inference or orchestration systems in real-time, distributed, or resource-constrained environments. ### Trase Specific Benefits:

Career track opportunity with potential for rapid advancement with strong performance as the firm grows

100% employer paid, comprehensive health care including medical, dental, and vision for you and your family. - Paid maternity and paternity for 14 weeks at employees' normal pay. - Unlimited PTO, with management approval. - Opportunities for professional development and continued learning. - Optional 401K, FSA, and equity incentives available. - Mental health benefits are available through Tara Mind. Salary Range: $250,000-300,000. This represents the typical salary range for this position based on experience, skills, and other factors. #LI-RCP