Responsibilities
- Build, deploy, and optimize AI agents that engage in enterprise-grade conversations.
- Design & develop next-gen multimodal LLM architectures (LLMs, speech, vision, reinforcement learning)
- Explore optimal trade-offs between model quality and efficiency when translating research into practical solutions
- Refine training paradigms for real-world applications
- Integrate AI agents with enterprise systems via APIs, databases, and automation tools.
- Experiment rapidly to improve AI-driven interactions, response quality, and automation capabilities.
- Collaborate with cross-functional teams (engineering, research, and product) to shape Eloquent AI’s roadmap.
- Monitor and improve agents’ performance via user simulations and evaluations.
Requirements
- 3+ years of experience in software development, AI engineering, or NLP in a production environment.
- Strong proficiency in Python, with experience in frameworks like PyTorch and TensorFlow.
- Experience working with LLMs or large computer vision models, or generative AI models, including fine-tuning and inference optimization.
- Familiarity with APIs, cloud infrastructure (AWS, GCP, or Azure), and enterprise integrations.
- Ability to prototype, experiment, and iterate quickly to improve AI agents.
- Strong problem-solving skills and the ability to work closely with customers to refine AI solutions.
- Solid mathematical foundation of machine learning and deep learning techniques
Nice to Have
- You have experience with prompt engineering, parameter-efficient fine-tuning (PEFT), retrieval-augmented generation (RAG), reinforcement learning for LLMs.
- You’ve published AI research in top tier AI conferences like: NeurIPS, ACL, SIGIR, ICML and ICLR.
- You’ve contributed to open-source NLP projects.
- You’ve worked in a fast-paced startup environment and thrive in rapid iteration cycles.
Work Arrangement
Remote (Worldwide) — San Francisco
