NLP Research Engineer

Forestreet provides business intelligence by analyzing public data with Machine Learning and NLP techniques. The NLP Research Engineer will design, train, and evaluate NLP models and embedding pipelines, build labeled datasets and evaluation frameworks, and prepare research outputs for ETL Engineers to productionize. The role also involves prototyping agentic LLM solutions, defining handoff contracts, and mentoring junior team members.

Responsibilities:

• Design, train, and evaluate NLP models and embedding pipelines, using both traditional techniques (TF-IDF, topic modeling, rule-based/statistical NLP) and Sentence Transformers where they earn their complexity
• Own the vector space end-to-end: procuring training data, model/architecture choice, dimensionality trade-offs, indexing strategy in FAISS, drift monitoring, and rigorous evaluation using precision/recall, clustering quality, human-in-the-loop review) rather than relying on •vibes• or leaderboard scores
• Right-size every model decision: default to CPU-friendly and lightweight approaches, and build the evidence-based case required before reaching for GPU infrastructure
• Build and maintain labeled datasets — including guidelines, annotation QA, and versioning — that are clean and well-documented enough for ETL Engineers to productionize without guesswork
• Prototype using agentic LLMs, while maintaining independent and conscious judgment on when an LLM-based approach is the right/wrong tool for the job
• Partner with ETL/DevOps Engineers to define handoff contracts: model artifacts, inference expectations, latency/cost budgets, and schema for outputs (via Pydantic) that plug cleanly into the existing pipeline
• Mentor junior team members on NLP fundamentals and evaluation rigor, and contribute to code reviews on the research side of the pipeline