Machine Learning Engineer (AI)

Location: On-site in Dublin, California, United States

Company

AI product development focused on enterprise generative AI and domain-specific models.

What you will do

• Design and develop data processing pipelines for data extraction, filtering, labeling, and analysis.
• Implement machine learning models that improve data quality and diversity, including quality classifiers, document layout models, and code verification models.
• Lead data acquisition engineering projects covering web crawling, data ingestion, and processing.
• Develop scalable distributed systems capable of handling terabytes of data.
• Architect data indexing and search algorithms and maintain backend storage services using key-value databases and synchronization systems.
• Deploy solutions in Kubernetes infrastructure-as-code environments and perform routine system checks.

Requirements

• BS, MS, or PhD in Computer Science or a related field.
• Proficiency in at least one deep learning framework, such as PyTorch.
• Experience training machine learning models for text or vision problems.
• Strong expertise in stateful distributed systems, data processing, and large-scale data pipelines.
• Proficiency in Python or another programming language commonly used in machine learning, with the ability to write clean, maintainable code.
• Experience with distributed workloads and infrastructure such as multiprocessing, Ray, Docker, and Kubernetes, plus strong problem-solving skills when addressing data anomalies and bias.

Nice to have

• Active GitHub contributions.
• Experience building large-scale datasets and bespoke data processing libraries.
• Familiarity with data crawling, collection, or processing tools such as Scrapy, Selenium, VPNs, Hadoop, and Datasketch.
• Multilingual ability and familiarity with state-of-the-art techniques for preparing AI training data.

Culture & Benefits

• Full-time work within an environment focused on diversity and inclusion.
• Mentorship, knowledge exchange, and constructive feedback.
• Continuous learning and professional development opportunities.
• Encouragement of curiosity, creativity, and exploration beyond conventional approaches.