AI/ML Intern, Long-Term Memory & Multimodal SLMs for Automotive Agents

At Mercedes-Benz Research & Development North America (MBRDNA), we are committed to delivering world-class automotive technologies that push the boundaries of what is possible. Our teams of highly skilled engineers and designers use cutting-edge software and technology, to enhance the driving experience and reduce environmental impact.

Position Overview

This internship will start in January of 2027.

The Mercedes-Benz AI Experiences department is seeking an AI/ML Intern for Spring and Summer 2027 (both terms) to join the MB.Intelligence Context team. You will build on-device long-term memory for automotive agents using small language models (SLMs), Liquid AI's multimodal LFM3-Omni model (text, image, video, and audio inputs), and trigger-based activation, helping deliver personalized, privacy-preserving experiences in Mercedes-Benz vehicles.

Job Responsibilities\:

• Design and implement multimodal long-term memory (RAG) for automotive agents in resource-constrained in-vehicle environments.

• Fine-tune LFM3 models, especially LFM3-Omni-2B, and other small language models for memory, preference extraction, and function calling (SFT, LoRA/QLoRA).

• Apply reinforcement learning (e.g., DPO, GRPO) to improve memory, activation, and tool-use decisions.

• Build trigger-based activation from multimodal cabin and vehicle signals, using decision models (e.g., LFM3 d1-omni-2B) and lightweight classifiers.

• Build classifiers for turn detection, PII detection, and intent routing.

• Optimize and profile models for on-device deployment (quantization, distillation) on in-vehicle hardware.

• Build evaluation frameworks for memory, retrieval, and trigger accuracy, and present findings to Mercedes-Benz teams.