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Articul8
Dublin, California, United States
Source: Articul8 careers · View original posting
From Articul8's posting. “We” and “our” refer to the employer.
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.
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.
Key Competencies
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.
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