Uniphore is one of the largest B2B AI-native companies—decades-proven, built-for-scale and designed for the enterprise. The company drives business outcomes, across multiple industry verticals, and enables the largest global deployments.

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 on developing new foundational machine learning technology behind a category-defining product – Business AI will democratize the benefits of cutting-edge machine intelligence at scale to more customers. If you are ready to take on the challenge of building the foundation model that enables a wide suite of capabilities around AI agent optimization, training of small and efficient foundation models, human-AI interaction, and agentic evaluation, etc., come join us!

Responsibilities

AI System Design & Implementation

• Design and implement AI systems capable of handling complex workflows, including multi-step reasoning, planning, and decision making.

• Explore and apply various orchestration patterns and architectures to solve business problems effectively.

• Translate abstract requirements into reliable AI behaviors under real operational constraints such as latency, cost, and safety.

Experimentation & Evaluation

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

• Define and implement evaluation strategies for agentic systems, including task success, robustness, failure recovery, and hallucination analysis.

• Develop lightweight benchmarks, simulations, and offline/online evaluation loops to guide rapid iteration and decisionmaking.

Model Optimization & SLMs

• Finetune and adapt small and medium-sized foundation models using techniques such as PEFT, SFT, distillation, and reinforcement learning.

• Balance performance, latency, and cost for enterprisegrade workloads.

• Contribute to model selection decisions (SLMs vs. larger models) based on usecase requirements and empirical evidence.

Applied EndtoEnd Delivery

• Own bounded, endtoend AI workflows from problem framing through deployment where this accelerates impact.

• Partner with engineering teams on integration, monitoring, and lifecycle management.

• Contribute to production readiness without acting as a fulltime platform or infrastructure owner.

Technical Leadership

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

• Mentor junior scientists on experimentation methodology, evaluation design, and practical GenAI system development.

• Influence roadmap and technical direction through evidencebacked recommendations.

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, Operations Research, Statistics, or related fields

• Applied ML / GenAI Experience: 5+ years designing, building and iterating on ML systems, with hands‑on experience using frontier models and open-source models.

• Experience in translating business impact into quantitative metrics and designing experiments and statistical analysis of results

• Experience in working with cross-functional teams to deliver ML capabilities in customer-facing products and maintaining ML quality

• Strong communication skills, verbal and written

Preferred Qualifications

• Strong experience in model training, building autonomous agentic systems and agentic workflows, and rigorous evaluation of agentic systems

• Demonstrated track record of excellence (e.g., publications at top-tier peer-reviewed conferences or journals) or significant product impact with highly technical solutions, and desire to grow

• Experience at fast-growing companies or startups, or working in an agile environment