AI Research Engineer
Explore how AI systems remember, retrieve, and reason across large contexts.
About the Role
LogicKrew is building AI-native developer infrastructure. A core component of our product is context and memory how AI-assisted systems store, retrieve, and reason about information across time, teams, and codebases. We are looking for a research intern who wants to go deep on these problems.
This is not a survey role. You will work directly on implementation research building, testing, and evaluating systems related to LLM memory, advanced RAG architectures, multi-threaded context pipelines, and retrieval optimization.
What makes this different
You will work directly with the founder on applied memory and retrieval problems that matter for real product usage. This is implementation-first research you will build things, measure them, and iterate.
Core Research Areas
LLM Memory Systems
- Agent memory architectures
- Long-context LLMs
- Memory optimization strategies
- Context window compression
- Episodic & semantic memory
Advanced RAG
- Retrieval-Augmented Generation
- Hybrid search strategies
- Knowledge graph integration
- Context compilation pipelines
- Chunking & embedding strategies
Concurrency & Processing
- Multi-threaded retrieval pipelines
- Parallel context processing
- Async indexing systems
- Context routing & scheduling
- Real-time ingestion pipelines
Additional Focus Areas
- Prompt optimization and context engineering
- Repository-level code understanding
- Indexing algorithm evaluation
- Model retrieval metric design
- Vector store performance benchmarking
- Cross-session memory persistence
- Contextual relevance scoring
- Agentic memory feedback loops
What We're Looking For
Technical Background
- Experience with LLMs and embeddings
- Understanding of RAG pipelines
- Python proficiency (or strong desire to learn)
- Familiarity with vector databases
- Multi-threading or async programming
Soft Skills & Mindset
- Intellectual curiosity and rigor
- Ability to read technical papers and implement ideas
- Comfortable with ambiguity and iteration
- Ownership over research deliverables
- Clear written and verbal communication
Ideal Backgrounds
- ML Engineers with LLM experience
- Master's or PhD students in AI/CS/NLP
- Active open-source contributors in AI ecosystem
- Systems researchers building retrieval systems
- Self-taught engineers with strong AI project portfolios
- Researchers interested in applied, product-level work
Our Expectations
You will be expected to understand problems before implementing solutions, read provided documentation and technical papers, ask thoughtful questions, validate and test your implementations, and communicate research findings and blockers clearly. We care about your reasoning process as much as your final output.
Benefits & Access
- AI model credits and developer tooling
- Cloud credits via startup programs
- Direct mentorship from the founder
- Access to technical learning resources
- Exposure to modern AI product engineering
Future Opportunities
LogicKrew is currently bootstrapped. Exceptional contributors may be considered for continued collaboration and future roles as the company evolves and secures growth.