About the role:
NLPatent is an industry leading AI-first patent research platform that was an early mover in the application of Large Language Models. We use a combination of proprietary and off-the-shelf machine learning models and NLP techniques to help our users answer patent related research questions such as "Is my invention patentable?". Increasingly, we're using generative LLMs to build agentic workflows that answer research questions without any human intervention.
As a software engineer at NLPatent, and one of the early hires for the engineering team, you'd be key in building the core features and foundations to scale out the platform. You'd work closely alongside NLPatent's CTO and senior engineers in planning and executing product development.
Requirements
- 2+ years of commercial engineering experience
- Python web development experience
- Experience with Django
- Experience with Docker and containerized web applications
- Experience with relational DBs
- Proficiency with Git
- Willing to work 3 days per week onsite in the office
Bonus points for:
- Exposure to NLP - in particular semantic search systems
- Experience working with LLMs
- Frontend experience (React)
- Experience with Elasticsearch or Opensearch
- AWS Experience
Benefits
- Flexible working
- Pension
- Training & Development
- Stock Option Plan
- Work from home budget
- 25 days of paid annual leave
Top Skills
What We Do
NLPatent is an industry leading AI-based patent search and analytics platform trusted by Fortune 500 companies, Am Law 100 firms, and research universities around the world. The platform takes an AI-first approach to patent search; it's built from a proprietary Large Language Model trained on patent data to truly understand the language of patents and innovation. Users simply describe their invention in full sentences and conceptually relevant results are generated instantly; consistently outperforming human experts on speed and accuracy. The system is simple, intuitive, and iterative. Best of all, it explains the relevant sections of each patent it identifies, removing the "black box" often experienced by other AI-based platforms.
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