Machine Learning Engineer - Data Pipeline

Articul8•Dublin, CA

20h

About The Position

At Articul8 AI, we relentlessly pursue excellence and create exceptional AI products that exceed customer expectations. We are a team of dedicated individuals who take pride in our work and strive for greatness in every aspect of our business. We believe in using our advantages to make a positive impact on the world and inspiring others to do the same. We are seeking machine learning engineers to join our team full-time. As part of your role, you will help us build pipelines of data collection, data extraction, data filtering/synthetic data generation and data analysis. You will own all work related to acquiring high-quality data to power the training of our domain-specific models end to end. You will work closely with other researchers and engineers to empower our next generation of domain-specific models. We value rapid prototyping, iterating, and shipping new systems quickly. By joining our team, you become part of a community that embraces diversity, inclusiveness, and lifelong learning. We nurture curiosity and creativity, encouraging exploration beyond conventional wisdom. Through mentorship, knowledge exchange, and constructive feedback, we cultivate an environment that supports both personal and professional development. Your future experience at Articul8 will include continuous learning and growth opportunities as we embark on an exciting journey to disrupt the status quo. If you're excited about joining a team that's passionate about making a difference, we want to hear from you. If you're ready to join a team that's changing the game, apply now to become a part of the Articul8 team. Join us on this adventure and help shape the future of Generative AI in the enterprise.

Requirements

BS/MS/PhD in Computer Science or a related field.
Proficiency in at least one deep learning framework, such as PyTorch.
Experience in machine learning projects in text or vision, e.g., has trained machine learning models to tackle a specific problem.
Strong expertise in large stateful distributed systems and data processing.
Strong proficiency in building large-scale data processing pipelines, familiar with distributed workload (e.g., multiprocessing, Ray, Docker, Kubernetes).
Proficiency in at least one programming language commonly used in machine learning, such as Python and ability to write clean, maintainable code.
Excellent problem-solving skills and attention to detail, especially when handling data anomalies and biases to further improve data quality.

Nice To Haves

Active Github contributions are a big plus.
Experience in building large-scale datasets.
Familiar with at least one of the following tools for data crawling (e.g. Scrapy), data collection (e.g., VPNs, Selenium), data processing (e.g., Hadoop, Datasketch).
Building bespoke data processing libraries from scratch.
Keeping up with state-of-the-art techniques for preparing AI training data.
Organizing and meticulously bookkeeping data across multiple clouds, of multiple modalities, and from many sources.
Multilingual which contributes to enriching the language diversity crucial for robust model training.

Responsibilities

Design and develop data processing pipelines, including data extraction, data filtering, data labeling, etc.
Implement machine learning models to improve the quality and diversity of data (especially in the data extraction stage), e.g., quality classifier, document layout model, code verification model, etc.
Own and lead engineering projects in the area of data acquisition, including web crawling, data ingestion, and processing.
Collaborate with our Applied Research, Technology, and Architecture teams to ensure smooth data flow and system operability.
Develop and deploy highly scalable distributed systems capable of handling terrabytes of data.
Architect and implement algorithms for data indexing and search capabilities.
Build and maintain backend services for data storage, including work with key-value databases and synchronization.
Deploy solutions in a Kubernetes Infrastructure-as-Code environment and perform routine system checks.