NLP / Machine Learning Engineer (Financial Text AI)
Hiretop · Copenhagen, Denmark ·
- Category
- ML ai
Hiretop · Copenhagen, Denmark ·
P.S. If you're an exceptional match for this position but prefer working remotely and can travel to Copenhagen from time to time, we'd still love to hear from you. Let's discuss the possibilities.
We're looking for an NLP Engineer to join an innovative Danish startup building AI-powered technology that transforms complex financial documents into structured, production-ready data used in real-world applications.
The team develops specialized NLP models capable of extracting information such as company earnings, forecasts, M&A transactions, share buybacks, executive changes, and many other financial events from thousands of documents processed every day.
This is a small, highly technical team where engineers own problems end-to-end. We're looking for an applied NLP/ML engineer who enjoys taking models all the way from data collection and experimentation to production deployment and optimization.
The ideal candidate combines strong experience in modern NLP with solid software engineering skills, including backend development, data pipelines, and model deployment. You'll work across the entire ML lifecycle—from improving data quality and training datasets to optimizing inference performance and building internal tools that help the team iterate faster.
Project Stack
Python 3.12+, PyTorch, HuggingFace Transformers, ONNX Runtime, GCP, Hydra, Pydantic, uv, DVC, Streamlit, spaCy
- MSc or PhD in Computer Science, Computational Linguistics, or a related field (or equivalent research/industry experience in NLP or ML)
- Experience training and deploying transformer-based NLP models, especially for NER or structured extraction
- Strong Python skills. Comfortable with PyTorch, HuggingFace Transformers and Pydantic
- Experience with data pipelines at scale. You've wrangled large, messy datasets and built reproducible workflows
- Familiarity with GCP/Azure/AWS or similar cloud ML infrastructure
- Comfortable with Docker for packaging and deploying ML workloads
- You care about data quality and understand that label quality drives model quality
- Pragmatic engineering instincts. You ship working systems, not over-engineered abstractions
Bonus Points
- Experience with ONNX Runtime optimization (quantization, OpenVINO, hardware-specific compilation)
- Experience with spaCy, LightGBM or other classical NLP/ML tools alongside deep learning
- Familiarity with financial texts and/or equities analysis, research or trading
- Track record of building internal tools that accelerate team velocity