NLP Engineer

Chronograph was founded to bring next-generation technology to private capital markets. Through our suite of cloud-based analytics and data management solutions, we help many of the world’s largest and most sophisticated venture capital, private equity, and credit funds understand their investment performance in unprecedented detail, with over $19 trillion of AUM monitored via our solution suite.

At Chronograph, we get to go “behind the scenes” and work directly with investors who are driving some of the most impactful changes across high growth start-ups, global infrastructure and renewable energy, growth equity, and all other private capital strategies. The firm is backed by The Carlyle Group, Nasdaq Inc., and Summit Partners, and has seen continuous rapid growth since its founding in 2016.

The Opportunity

Bring your expertise to a highly collaborative, creative, and innovative team with a market-leading technology product suite. We are seeking a highly motivated NLP Data Scientist with Software Engineering capability to join our Machine Learning team to help design and develop our NLP pipelines .

You will work closely with internal stakeholder teams of product owners and private equity analysts, with a focus on model engineering, continuous model improvements, ML pipeline deployments, and human in the loop capabilities. This position will report to the Machine Learning Lead, with engagement across the organization.

This is an exceptional opportunity for a driven, detail-oriented top performer to become directly engaged with several of the world’s most sophisticated private equity investors and produce immediate impact within a globally-focused financial technology firm.

Even if you do not meet all criteria, we would still encourage you to apply! Chronograph offers an entrepreneurial environment where you will be able to proactively identify opportunities to develop and strengthen our Machine Learning function.

As an NLP Engineer at Chronograph, you will:

• Evaluate & Experiment with classical ML models in an NLP context (Logistic Regression, random forests, ensembles, etc.), as well as Deep Learning based models like Transformers and Large Language Models
• Research Scientific Papers to find better or novel approaches and algorithms to improve model performance
• Set up pipelines for model evaluation / comparison in order to progress Machine Learning capabilities at Chronograph.
• Work on improving models using unlabeled data through cutting edge algorithms eg. self-supervised learning, semi-supervised learning, active learning, or weak supervision with specific focus on NLP and Information Extraction
• Work with devOps, design, and full-stack teams to deploy models to production and integrate ML features