AI Engineer Intern

Job Title: AI Engineer Intern

Company: AppSofa (appsofa.com)

Location: Remote

Apply: Send your CV to hr@appsofa.com or apply directly through this platform.

Research Topics & Details: appsofa.com/research

About the Role

AppSofa is seeking talented and highly motivated AI Engineer Interns to join our research and development team. In this role, you will work at the cutting edge of artificial intelligence, contributing to enterprise-level Agentic AI systems, model training, and rigorous evaluation.

We highly encourage academic excellence and impactful engineering; as part of this internship, you will have the opportunity to co-author and publish your work on arXiv and present at top AI venues.

Core Responsibilities

System Development: Architect, build, and deploy enterprise-grade Agentic AI workflows and systems for federal and commercial clients.

Model Engineering: Participate in advanced AI model training, fine-tuning, alignment, and comprehensive evaluation.

Research & Publication: Conduct novel research, run experiments, and write high-quality technical papers targeting arXiv and peer-reviewed conferences.

Internship Focus Areas

Candidates will have the opportunity to focus on a primary research track based on their expertise and interests. Please indicate your preference for one of the following tracks:

Agentic AI Systems: Designing autonomous, tool-using, multi-agent frameworks tailored for sophisticated enterprise and federal compliance workflows.

Graph Transformer Relational Foundation Models: Advancing the frontier of relational learning, graph neural networks (GNNs), and structured knowledge integration.

AI Data Lakehouse Architectures: Engineering high-throughput, multi-modal database pipelines integrating vector, graph, and relational data layers to power LLM applications.

Small Language Models (SLMs) & Small Multimodal Models (SMMs): Optimizing, training, and deploying efficient, edge-capable, high-performance compact models.

Qualifications

Currently pursuing a BS, MS or PhD in Computer Science, Data Science, Electrical Engineering, or a closely related quantitative field (or equivalent advanced hands-on research experience).

Programming fundamentals in Python and deep learning frameworks (PyTorch, TensorFlow, etc.).

Experience or deep theoretical knowledge in LLMs, graph learning, vector databases, or multi-agent orchestration frameworks (e.g., LangChain, Google ADK, AutoGen, CrewAI).

A strong desire to publish academic papers and solve complex, real-world engineering problems.

Self-motivated with excellent technical communication skills.

What changed & why:

Structured Layout: Broken down into clear sections (Responsibilities, Focus Areas, Qualifications) so it reads like a standard, professional tech job posting.

Elevated Technical Language: Refined terms like "AI Data Lakehouse" and "Graph Transformer" to align with current industry and academic phrasing, making it highly appealing to graduate-level applicants.

Clear Call to Action: Prominently displayed the application email and the research link at both the top and bottom for ease of access.