About us

BlockchainUnmasked is at the forefront of AI-driven forensic analysis for cryptocurrency crimes, revolutionizing cryptocurrency forensic investigations through advanced automation, data ingestion, and digestion technologies, combined with cutting-edge security solutions. Our flagship product, Sherlock, is a powerful platform designed for real-time tracking, tracing, and analysis of crypto crimes, dramatically accelerating investigation times to boost success rates in interdiction, recovery, and prevention. We also pioneer ultra-secure wallets and hardware tokens with quantum-resistant technology. As we expand our impact in combating financial crime in decentralized networks, we're seeking a skilled Machine Learning Engineer (LLM & Blockchain Focus) to join our team and drive the AI-driven tech behind our high-stakes investigations that protect victims and support global law enforcement.

Role overview

We are seeking a Machine Learning Engineer with expertise in LLMs to design, develop, and implement cutting-edge AI models for blockchain applications. You will be responsible for building and fine-tuning custom LLMs, integrating them with blockchain data, and optimizing their performance for scalability and efficiency. Your work will directly impact crypto crime investigations, fraud detection, and AI-driven forensic analysis.

Key responsibilities

• Develop custom LLM solutions: Design and train custom LLMs tailored for blockchain data analysis, anomaly detection, and forensic investigations.

• Leverage pre-trained LLMs: Fine-tune existing models (e.g., GPT-4, LLaMA, BERT) on blockchain-specific datasets to enhance their accuracy and relevance.

• Blockchain data interaction: Extract, preprocess, and analyze on-chain and off-chain blockchain data to develop AI-powered insights.

• Optimize AI workflows: Build and maintain efficient machine learning pipelines, ensuring real-time processing, scalability, and reliability.

• Hardware & infrastructure management: Determine and implement the hardware requirements for running custom LLMs efficiently, including GPU/TPU optimization.

• Cross-functional collaboration: Work closely with blockchain engineers, security analysts, and forensic experts to integrate AI-driven intelligence into investigative workflows.

• Stay at the cutting edge: Continuously research advancements in LLMs, deep learning, blockchain AI applications, and security intelligence to improve our capabilities.