Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Designing, building, and deploying machine learning models for threat detection and narrative analysis capabilities with an accent on NLP, social network analysis, and large-scale data processing. Focus on researching and implementing state-of-the-art techniques in natural language processing and developing models for detecting coordinated inauthentic behavior.

Location: Remote (Mexico)

Company

hirify.global is a full-service consulting firm that was founded on the premise of delivering predictable outcomes and high-quality solutions to our clients.

What you will do

• Design, develop, and deploy machine learning models for threat detection, narrative analysis, and anomaly identification across large-scale datasets.
• Build and optimize NLP pipelines for analyzing text data from diverse sources.
• Collaborate with data engineers to develop scalable ML infrastructure and pipelines.
• Research and implement state-of-the-art techniques in natural language processing, including transformer-based models and large language models.
• Develop models for detecting coordinated inauthentic behavior, bot networks, and influence operations.
• Monitor model performance, conduct A/B testing, and continuously improve model accuracy and efficiency.

Requirements

• 5+ years of professional experience in machine learning engineering or a related role.
• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX.
• Deep expertise in NLP, including experience with transformer architectures, embeddings, and text classification.
• Experience building and deploying ML models in production environments.
• Proficiency with cloud platforms (AWS, GCP, or Azure) and MLOps tools.
• Strong understanding of data structures, algorithms, and software engineering principles.

Nice to have

• Experience with social network analysis, graph neural networks, or community detection algorithms.
• Background in information security, OSINT, or misinformation research.
• Familiarity with LLM fine-tuning, prompt engineering, or retrieval-augmented generation (RAG).

Culture & Benefits

• Remote Work Opportunities
• Flexible Work Hours
• Professional Development Opportunities