Machine Learning Engineer (AI)

Location: Dublin, California, United States; on-site

Company

Develops AI products and domain-specific generative AI models for enterprise customers.

What you will do

• Design and develop pipelines for data extraction, filtering, labeling, ingestion, and analysis.
• Build machine learning models that improve dataset quality and diversity, including quality classifiers, document layout models, and code verification models.
• Lead data acquisition engineering projects involving web crawling, data ingestion, and processing.
• Develop highly scalable distributed systems for processing terabytes of multimodal data.
• Architect data indexing and search algorithms and maintain backend storage services, including key-value databases and synchronization.
• Deploy solutions in Kubernetes infrastructure-as-code environments and collaborate with research, technology, and architecture teams.

Requirements

• BS, MS, or PhD in Computer Science or a related field.
• Proficiency in at least one deep learning framework, such as PyTorch, and one machine learning programming language, such as Python.
• Experience with machine learning projects in text or vision.
• Strong expertise in stateful distributed systems, data processing, and large-scale data processing pipelines.
• Experience with distributed workloads and infrastructure such as multiprocessing, Ray, Docker, or Kubernetes.
• Strong problem-solving skills and attention to detail when addressing data anomalies, bias, and data quality.

Nice to have

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

Culture & Benefits

• Environment focused on diversity, inclusion, curiosity, creativity, and lifelong learning.
• Mentorship, knowledge exchange, and constructive feedback.
• Continuous learning and professional development opportunities.
• Rapid prototyping, iteration, and shipping of new systems.