Senior Staff AI Scientist

Uniphore is building the world's best-in-class Business AI platform to enable business users to leverage the advances of artificial intelligence to solve problems in their specific business processes. Uniphore provides a composable, sovereign, and secure solution to business users, and drives AI adoption in organizations with innovative solutions to building, serving, and optimizing these AI solutions. 

Location: Palo Alto, CA 

You will be working at the core of a category-defining product — building the agent learning platform and SLM AI flywheel that powers our Business AI cloud. This is the system that closes the loop: AI agents operate in production, capture real-world signal, and continuously improve through automated fine-tuning and evaluation. If you are ready to own the full ML stack behind agentic AI at enterprise scale — from orchestration architecture through model optimization, evaluation infrastructure, and production delivery — come join us. 

Responsibilities 

Agentic AI Architecture & Orchestration 

• Design and implement production-grade agentic systems capable of multi-step reasoning, planning, tool use, and decision-making under real operational constraints (latency, cost, safety). 

• Own the orchestration layer of the agent learning platform: agent memory, inter-agent communication, failure recovery, and reliability patterns at enterprise scale. 

• Translate abstract product requirements into reliable AI behaviors and set the architectural standards the team builds against. 

Agent Learning & SLM Optimization 

• Own the closed-loop learning pipeline: capturing production signal from deployed agents, triggering fine-tuning cycles, and gating model promotion into production. 

• Fine-tune and adapt small and medium-sized foundation models using techniques such as PEFT, SFT, distillation, and reinforcement learning (RLHF, DPO). 

• Drive model selection decisions (SLMs vs. larger models) based on use-case requirements, latency SLAs, and empirical evidence. 

Evaluation & Experimentation 

• Define and build evaluation strategy for agentic systems: task success metrics, trajectory evaluation, hallucination analysis, and regression detection across the learning flywheel. 

• Develop offline and online evaluation loops — including LLM-as-judge frameworks — that guide rapid iteration and provide the ground truth signal the flywheel depends on. 

• Lead systematic experimentation across prompts, agent configurations, model variants, and tool integrations. 

End-to-End Delivery & Production Ownership 

• Own bounded, end-to-end ML workflows from problem framing through deployment, monitoring, and lifecycle management. 

• Partner with engineering on integration, observability, and production readiness — without acting as a full-time infrastructure owner. 

• Identify systemic gaps across the ML stack (accuracy, latency, cost, reliability) and lead the work to close them. 

Technical Leadership 

• Act as the technical reference point for agentic AI and SLM best practices across the team. 

• Drive cross-functional alignment with product and engineering through evidence-backed technical recommendations that influence the roadmap. 

• Mentor senior and mid-level engineers on experimentation methodology, evaluation design, and production ML system development. 

We're Eager to Work With 

• Those who want to join our mission to democratize AI to more business users. 

• Individuals with an unwavering customer focus, committed to crafting experiences that delight. 

• Those who embrace excellence and consistently deliver top-tier work. 

• Innovators who thrive on creative thinking and daring to tread new paths. 

• Owners at heart, ready to take responsibility and drive results. Individuals of the highest integrity, who foster trust and honesty. 

Minimum Qualifications 

• Education: MS or PhD in Computer Science, Machine Learning, Statistics, or related field 

• Experience: 8+ years designing, building, and operating production ML systems, with hands-on experience with frontier and open-source models 

• Proven track record owning agentic AI systems or closed-loop model improvement pipelines in production — not prototype quality 

• Deep experience with LLM or SLM fine-tuning: SFT, RLHF/DPO, data curation, and rigorous evaluation design 

• Experience translating business impact into quantitative metrics and designing statistically sound experiments 

• Track record of influencing technical decisions beyond your immediate team — through design docs, architectural reviews, or cross-functional alignment 

• Strong communication skills, verbal and written, with ability to present technical strategy to non-technical stakeholders 

Preferred Qualifications 

• Experience designing evaluation frameworks for agentic systems (trajectory eval, task success, robustness benchmarks 

• Demonstrated influence at org or platform level: architectural standards or platform decisions that multiple teams built against 

• Familiarity with agentic orchestration frameworks ( LangGraph, or equivalent) 

• Background in enterprise NLP, conversational AI, or contact center / CX domains 

• Publications at top-tier peer-reviewed venues or significant open-source contributions in relevant areas 

• Experience at fast-growing companies or in agile, high-ownership engineering environments