Senior Machine Learning Engineer

at  Canoe Intelligence

COMPANY: Canoe Intelligence
WEBSITE: https://canoeintelligence.com/
TITLE: Senior Machine Learning Engineer
LOCATION: New York City (hybrid) or Fully Remote in the United States
SALARY: $140,000 - $210,000 dependent on level of hire (based on NYC, will be adjusted for GEO)

WHO WE ARE:

Canoe is reimagining alternative investment data processes for hundreds of leading institutional investors, capital allocators, asset servicing firms and wealth managers. By combining industry expertise with the most sophisticated data capture technologies, Canoe’s technology automates the highly-frustrating, time-consuming, and costly manual workflows related to alternative investment document and data management, extraction and delivery. With Canoe, clients can refocus capital and human resources on business performance and growth, increase efficiency, and gain deeper access to their data. Canoe’s AI-driven platform was developed in 2013 for Portage Partners LLC, a private investment firm.
Canoe is an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law

THE ROLE:

We are looking for a Senior Machine Learning Engineer to dive into a hyper growth startup developing models that improve our data processing pipeline and help us deliver new features to the market with unprecedented speed and accuracy.

WHAT YOU’LL DO:

• NLP Algorithm Development: Lead the design and implementation of state-of-the-art NLP algorithms, with a focus on information extraction, entity resolution, and entity disambiguation.
• Model Training and Optimization: Develop and train large language models for various NLP tasks, optimizing their performance for accuracy, efficiency, and scalability.
• Data Preprocessing and Feature Engineering: Work closely with data engineering teams to preprocess and engineer features from large datasets to enhance the performance of machine learning models.
• Entity Resolution and Disambiguation: Drive the development of solutions for entity resolution and disambiguation challenges, ensuring robust and accurate identification of entities within unstructured text.
• Collaboration with Cross-functional Teams: Collaborate with product managers, software engineers, and other stakeholders to integrate machine learning models into end-to-end solutions.
• Research and Innovation: Stay abreast of the latest advancements in NLP and machine learning, and actively contribute to research initiatives to enhance our technology stack.
• Code Review and Mentorship: Conduct code reviews to ensure code quality and provide mentorship to junior members of the machine learning team.