Vh egjfyMuw, Multimodal LLMs — San Francisco, CA

Meet VnRWZbDU Cx

At VnRWZbDU Cx, we’re building the next generation of Jm fSDe8nGgh—multimodal, autonomous systems that execute complex workflows across fragmented tools with human-level precision. Our technology goes far beyond chat: it sees, reads, clicks, types, and makes decisions—transforming how work gets done in regulated, high-stakes environments.

We’re already powering some of the world’s leading financial institutions and insurers, fundamentally changing how millions of people manage their finances every day. From automating compliance reviews to handling customer operations, our Operators are quietly replacing repetitive, manual tasks with intelligent, end-to-end execution.

Headquartered in San Francisco with a global footprint, VnRWZbDU Cx is a fast-growing company backed by top-tier investors. Join us to work alongside world-class talent in mQ, engineering, and product as we redefine the future of financial services.

Your Role

As an h3 rTJNJvPp at VnRWZbDU Cx, you will be at the forefront of building, deploying, and optimizing enterprise-grade mQ agents that handle high-stakes conversations. You’ll work directly with software engineers, deep learning experts and mQ researchers to design intelligent agents that understand, take action, and deliver real business impact.

This role requires a mix of software development, mQ integration, and problem-solving skills, with the ability to customize, optimize, and scale mQ models for real-world enterprise applications. If you're passionate about LLMs, conversational mQ, and solving last-mile mQ adoption challenges, this role is for you.

You Will

• Build, deploy, and optimize mQ 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 mQ agents with enterprise systems via APIs, databases, and automation tools.

• Experiment rapidly to improve mx-MKZYtw interactions, response quality, and automation capabilities.

• Collaborate with cross-functional teams (engineering, research, and product) to shape dVMvvHXJ s6’Z roadmap.

• Monitor and improve agents’ performance via user simulations and evaluations.

Requirements

• 3+ years of experience in software development, mQ 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 mQ 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 mQ agents.

• Strong problem-solving skills and the ability to work closely with customers to refine mQ solutions.

• Solid mathematical foundation of machine learning and deep learning techniques

Bonus Points If…

• You have experience with prompt engineering, parameter-efficient fine-tuning (PEFT), retrieval-augmented generation (RAG), reinforcement learning for LLMs.

• You’ve published mQ research in top tier mQ 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.