Principal Applied Scientist - Parloa

As a Principal Applied Scientist (f/m/d)*, you will define and drive technical strategy for our AI Agents while remaining hands-on with evaluation and improvement. This will involve developing evaluation tasks for agent behaviour, fine-tuning foundation models, researching and prototyping new AI Agent features and frameworks, and investigating issues affecting our customers. Youʼll develop a deep understanding of the behaviour of our models and AI Agents. Youʼll work embedded in a product engineering team, setting technical direction across the organization while leading by example on evaluating and improving our product - from improving the performance of our production agents to researching and developing new agentic systems

Required skills:

• 12-15+ years of experience as an Applied Scientist or closely related role, e.g. Applied Scientist or MLE.
• Deep expertise in enterprise-scale ML systems architecture with experience leading
• cross-team technical initiatives. Experience with LLMs, NLP, or information retrieval is a strong plus
• Strong proficiency in Python and experience with using using Python in production
• Ability to balance short-term velocity with long-term system resilience through smart
• trade-offs, owning the ML process end-to-end from strategy through productionisation.
• Demonstrated org-level or industry-level impact through technical leadership and strategic initiatives
• Strong spoken and written communication skills in English

Tech Stack

TypeScript, Node.js, React, Python, FastAPI, Kubernetes, MongoDB, Azure, Kafka, NATS, Databricksa

Responsibilities

• Define and drive technical strategy across multiple domains and org-wide applied science initiatives that help us improve our generative AI product suite
• Develop and evaluate production Agentic AI systems for real customer use cases
• Serve as trusted technical advisor to leadership, partnering with product engineering and sales, analyzing the performance of our AI products and prototyping new features
• Evaluate and train or fine-tune a variety of models, including LLMs
• Mentor Senior and Staff Applied Scientists, acting as a force multiplier while executing ML research and development projects that rapidly turn ideas into customer-facing features