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# Staff Data Scientist | NLP

- **Company:** Machinify, Inc.
- **Location:** La Grange, KY, United States
- **Experience:** Experienced
- **Salary:** $180,000.0 - $230,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Amazon Web Services, Microsoft Azure, Python (Programming Language), Machine Learning, Natural Language Processing, Named Entity Recognition, Open Source Technology, SQL Databases, Pytorch, Large Language Models, HuggingFace, Api Design, Spacy
- **Published:** August 30, 2026
- **Apply:** https://www.juju.com/job/00000000gpt2zi

## About the Role

High School Diploma/G.E.D., 3+ years of applied ML experience with a meaningful portion in NLP

Strong Python and the modern NLP stack: PyTorch or JAX, Hugging Face Transformers, spaCy, sentence-transformers

Hands-on experience fine-tuning transformer models (LoRA/QLoRA, instruction tuning, preference optimization) and/or building production RAG systems

Solid grounding in evaluation: knows the difference between BLEU/ROUGE/BERTScore/LLM-as-judge and when each is misleading

Comfortable with SQL, vector databases (pgvector, Pinecone, Weaviate, or similar), and one major cloud (AWS/GCP/Azure)

Clear written and verbal communication; can defend a modeling choice and also admit when a heuristic beats a m odel

Nice to have

Publications at ACL/EMNLP/NAACL/NeurIPS or strong open-source contributions

Experience with multilingual NLP, speech, or multimodal models

Background shipping LLM features in a regulated domain (healthcare, finance, legal)

## Description

We're hiring a Data Scientist focused on natural language processing to build models that turn unstructured text into product features and business insight. You'll own problems end-to-end - framing, data, modeling, evaluation, and shipping - and work closely with engineering and product to put your work in front of users.

What you'll do

Design and train NLP models for tasks like classification, entity extraction, retrieval, summarization, and semantic search

Fine-tune and evaluate LLMs (open-source and API-based); build RAG pipelines and agentic workflows where appropriate

Build robust evaluation harnesses - offline metrics, human-in-the-loop review, and online A/B tests

Partner with ML engineers to productionize models (latency, cost, monitoring, drift detection)

Turn ambiguous product questions into well-scoped ML problems and communicate tradeoffs clearly to non-technical stakeholders


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