Machine Learning Engineer

April 24

Apply Now

Get a free AI resume review

Syntax Data

WebsiteLinkedInAll Job Openings

Illuminating Insights

51 - 200

Description

• As ML Engineer you will be responsible for scaling Syntax’s set of classified public and private companies. Using our dataset of company descriptions, you will evaluate NLP algorithms and develop a ML model that will assign our proprietary classification labels to a high degree of accuracy. • Working closely with our software engineering team, you will deploy your ML models to the cloud and develop APIs to provide access to the model from other systems. • You will also have the opportunity to identify and apply novel applications of machine learning to other business processes.

Requirements

• A bachelor’s degree or higher in computer science, data science or related discipline. • 3+ years work experience in the machine learning field. • Prior professional experience working with NLP and machine learning. • Proficiency in programming languages commonly used in NLP, such as Python, and libraries/frameworks like TensorFlow, PyTorch, or spaCy and strong understanding of software engineering principles and best practices. • Strong knowledge of NLP techniques, including text data preprocessing (tokenization, stemming, and text normalization, etc.) and information extraction (summarization, and question answering, etc.). • Excellent problem-solving skills and the ability to work independently and collaboratively in a fast-paced, agile environment. • Strong communication skills and the ability to effectively articulate technical concepts to both technical and non-technical audiences.

Benefits

• Designing, developing, and implementing AI/ML models for natural language processing (NLP) applications. • Working with large datasets, selecting appropriate algorithms and techniques, training or fine-tuning models to achieve optimal performance, and deploying and monitoring model performance in production. • Working in a collaborative team environment across product management, data engineering, and software engineering teams.