Mercedes-Benz AI/ML Intern, Long-Term Memory & Multimodal SLMs for Automotive Agents 2027

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.

Table of Content

• Summary
• Benefits
• Requirements
• Application Deadline
• How To Apply

Summary

• Application DeadlineNot Specified
• Study LevelInternship
• SponsorMercedes-Benz
• Eligible CountryAll Countries

Benefits

• Coaching
• Employee discounts possible
• Employee mobile phone possible
• Health and wellness initiatives
• Mobility benefits
• Company pension scheme
• Accessibility / barrier-free facilities

Requirements

Minimum Qualifications:

• Master's or higher degree, in CS/CE/EE, Math, Statistics, or related fields
• Python and PyTorch; Git-based workflows
• Fine-tuning small language models (Hugging Face Transformers, TRL, PEFT, LoRA/QLoRA)
• Familiarity with reinforcement learning for language models (DPO, GRPO, or PPO)
• Familiarity with multimodal models (text, image, video, and audio)
• Familiarity with decision models and classifiers (e.g., calibrated decision models such as LFM3 d1)
• RAG, embedding models, and vector search
• Model optimization (quantization, distillation) and ML evaluation methodology
• Strong collaboration and written communication skills

Preferred Qualifications: 

• Hands-on experience with Liquid AI models (LFM2.5, LFM3)
• Edge LLM inference (llama.cpp, LiteRT, ExecuTorch, ONNX Runtime)
• Privacy-preserving ML, agentic AI/MCP, or Android embedded systems
• Always-on perception (wake-word, voice activity, or audio/visual event detection)

This position may involve access to export-controlled technology, hardware, or software under the Export Administration Regulations (EAR), 15 C.F.R. Parts 730-774 and possibly technical data, defense articles and defense services subject to the International Traffic in Arms Regulations (ITAR), 22 C.F.R. Parts 120-130. As such, Mercedes-Benz Research & Development North America may be required to obtain an export license or authorization in accordance with United States law.

Application Deadline

How To Apply

Interested and qualified? Go to Mercedes-Benz on tas-daimler.taleo.net to apply

For more information, kindly visit Mercedes-Benz Internship webpage.