Senior ML/NLP Engineer - Upgrade Advanced RAG System for Scientific Document Research - Contract to Hire
Looking for extensive search/retrieval experience
I have a working, well-tested RAG (Retrieval-Augmented Generation) system built in Python for processing 300+ scientific/technical PDF documents. The system already implements state-of-the-art retrieval techniques (hybrid search, hierarchical indexing, reranking, multi-hop reasoning, guardrails, evaluation) and has solid benchmarks.
Looking for someone who can take it to the next level — implementing specific upgrades grounded in the latest 2025-2026 research papers to improve retrieval quality and add new capabilities.
The work involves:
Upgrading existing components with newer algorithms backed by recent peer-reviewed research
Adding an agentic retrieval layer with proper reasoning loops
Benchmarking alternative approaches for key pipeline stages (parsing, embeddings) on our actual corpus before committing
Building a comprehensive evaluation and ablation suite with paper-ready visualizations
Writing clean, testable code that follows existing patterns in the codebase
This is NOT a build-from-scratch project.
looking for-
Deep hands-on experience building and optimizing RAG pipelines (not just tutorials — real systems)
Familiarity with latest retrieval research (2024-2026 papers)
Ability to read a research paper and implement the key ideas cleanly
Experience running proper ablation studies and benchmarks
Strong Python, PyTorch, Hugging Face Transformers
Apply tot his job
Apply To this Job
I have a working, well-tested RAG (Retrieval-Augmented Generation) system built in Python for processing 300+ scientific/technical PDF documents. The system already implements state-of-the-art retrieval techniques (hybrid search, hierarchical indexing, reranking, multi-hop reasoning, guardrails, evaluation) and has solid benchmarks.
Looking for someone who can take it to the next level — implementing specific upgrades grounded in the latest 2025-2026 research papers to improve retrieval quality and add new capabilities.
The work involves:
Upgrading existing components with newer algorithms backed by recent peer-reviewed research
Adding an agentic retrieval layer with proper reasoning loops
Benchmarking alternative approaches for key pipeline stages (parsing, embeddings) on our actual corpus before committing
Building a comprehensive evaluation and ablation suite with paper-ready visualizations
Writing clean, testable code that follows existing patterns in the codebase
This is NOT a build-from-scratch project.
looking for-
Deep hands-on experience building and optimizing RAG pipelines (not just tutorials — real systems)
Familiarity with latest retrieval research (2024-2026 papers)
Ability to read a research paper and implement the key ideas cleanly
Experience running proper ablation studies and benchmarks
Strong Python, PyTorch, Hugging Face Transformers
Apply tot his job
Apply To this Job