AI Research Intern at NVIDIA focusing on multi - modal AI and vision - language model development. Collaborating with engineers to advance cutting - edge machine learning research in Vietnam.
About the role
- Design and deploy production multi-agent AI systems using frameworks like LangGraph, OpenAI Assistants/Responses, Swarm, CrewAI, or AutoGen to solve complex business problems at scale
- Build production RAG pipelines implementing proven retrieval techniques including hybrid search, reranking, and knowledge graph integration for enterprise-grade performance
- Engineer and optimize prompts as versioned code using established techniques (Chain-of-Thought, ReAct, self-consistency) with rigorous testing and evaluation
- Implement comprehensive evaluation and monitoring pipelines with production KPIs, quality gates in CI/CD, and automated regression testing
- Architect scalable agentic workflows with persistent state management, error recovery, and production-grade reliability
- Deploy observability and debugging infrastructure using LangSmith, TruLens, DeepEval for production monitoring and issue resolution
- Integrate safety and compliance measures including PII detection, content filtering, guardrails, and security best practices
- Optimize system performance and costs through caching strategies, model routing, batch processing, and efficient resource utilization
- Build APIs and microservices for AI systems with proper versioning, documentation, and SLA management
- Champion AI-assisted development using tools like Claude Code, Augment, and Gemini CLI to maximize engineering productivity
- Stay current with relevant AI research - Read papers from top conferences (NeurIPS, ICML, ACL) to identify techniques applicable to our use cases
- Evaluate emerging techniques - Assess new methods for potential adoption, focusing on production viability and ROI
- Implement proven research concepts - Translate well-validated research into production when it provides clear value
- Conduct targeted experiments - Test specific hypotheses about system improvements with data-driven validation
Requirements
- 5+ years of software engineering experience with at least 2 years shipping LLM-powered products to production
- MS in Computer Science or equivalent experience (PhD is a plus but not required) with strong foundation in deep learning, NLP, or machine learning
- Solid understanding of transformer architectures, attention mechanisms, and modern deep learning techniques
- Ability to read and understand research papers to stay current with the field and identify useful techniques
- Proven track record shipping LLM applications with real users, SLAs, and production monitoring
- Production deployment of multi-agent systems using frameworks like LangGraph, OpenAI Assistants, or similar
- Experience building RAG pipelines at scale with vector databases, retrieval optimization, and quality metrics
- Expert-level prompt engineering with systematic testing and optimization approaches
- Building APIs and microservices for AI systems with proper error handling and scalability
- Distributed computing and storage experience (e.g., Apache Spark, Ray, cloud storage solutions)
- Strong software engineering practices including code review, testing, CI/CD, and documentation
- Active use of AI-assisted development tools like Claude Code, Augment, Gemini CLI to maximize productivity
- Experience with production ML/AI infrastructure including monitoring, debugging, and performance optimization
- Python expertise with async programming and modern web frameworks (FastAPI, Django)
- Vector database experience (pgvector, Pinecone, Weaviate, Milvus, or FAISS)
- LLMOps/AgentOps tools familiarity (LangSmith, TruLens, DeepEval, Giskard)
- SQL and search systems experience (PostgreSQL, MySQL, Elasticsearch, Solr, OpenSearch)
- Cloud platform proficiency (AWS, GCP, or Azure) with containerization and orchestration
- API design and microservices architecture for AI systems
- Version control and collaborative development practices with Git
Benefits
- Access to latest models, robust compute infrastructure, and top-tier development tools
- Support for attending major AI conferences to stay current with the field
- Focus on sustainable engineering practices and healthy team dynamics
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
AI Research Intern at NVIDIA developing generative models for biotechnology and computational science. Collaborating with a team to enhance AI model performance in drug discovery.
PI
PiecesApplied AI Scientist
Applied AI Scientist enhancing healthcare AI products with a focus on responsible AI and collaborative development. Utilizing advanced AI techniques to support healthcare providers' needs and streamline processes in patient care.
NO
Join Nokia Bell Labs' AI Accelerator Group as an intern. Contribute to innovative AI solutions for networks under expert guidance.
AI Research Engineer developing innovative learning systems at Campus. Leading research and collaboration to enhance student engagement through AI solutions.
RE
ResaroAI Scientist, LLM
Senior Data Scientist leading design and execution of evaluation frameworks for generative AI systems at Resaro. Focusing on large language models, applying scientific methods to ensure AI safety and effectiveness.
RE
ResaroSenior AI Scientist, LLM
Senior AI Scientist at Resaro, developing AI evaluation frameworks for generative AI systems. Engaging in deep learning applications, mentoring, and enhancing model performance metrics.
TI
Tri-global Solutions Group Inc.Senior AI Research Scientist – Human Behavior, Carbon Neutrality
AI Research Scientist at Toyota Research Institute developing generative AI models for understanding human behavior. Focused on machine learning and behavioral science for carbon neutrality research.
CL
Causeway Geotech LtdLead Data and AI Scientist
Lead Data & AI Scientist at Causeway building ML models and data platforms for construction software. Drive insights, implement data architectures, and collaborate with product, architecture, and engineering teams.
TS
Texas A&M University SystemAI Research Innovation Director
AI Research Innovation Director leading innovation and collaboration in AI research at TTI. Driving strategic partnerships and advancing AI applications in transportation domains.