Machine Learning Engineer

About UMO

UMO is a stealth-mode FinTech venture focused on evolving the experience of money by building a unified, AI-powered, and deeply human modern money platform across fiat, crypto, and investments, subject to regulatory approvals. This platform is designed to break down traditional financial barriers, enabling simpler and more adaptive interactions with financial services. UMO is currently developing its MVP and navigating licensing requirements with a multidisciplinary team of 100+ people representing over 20 nationalities. With headquarters in the UAE and offices in Portugal and Ukraine, the team is united by a shared ambition and a relentless focus on customer service.

Day-to-Day Responsibilities for a Machine Learning Engineer

• Financial Sentiment Analysis: Build and deploy NLP models to analyze news, social media (Twitter/X, Discord), and Reddit to gauge market sentiment for stocks and crypto assets.
• Named Entity Recognition (NER): Develop systems to identify and extract entities such as tickers, company names, wallet addresses, and transaction IDs from unstructured financial documents and chat logs.
• Automated Document Processing: Create pipelines to parse and extract data from financial statements, whitepapers, and regulatory filings (e.g., SEC filings) to assist in automated research.
• Fraud & Anomaly Detection: Implement NLP techniques to analyze transaction metadata and communication patterns to identify potential money laundering (AML) or fraudulent payment activity.
• Intelligent Customer Support: Build or fine-tune LLMs (Large Language Models) to power specialized chatbots capable of answering complex queries about portfolio performance, crypto protocols, or trading rules.
• Search & Discovery: Optimize internal search engines using semantic search and embeddings to help users find relevant financial instruments or transaction history.
• Model Lifecycle Management: Manage the full MLOps lifecycle, including data labeling for financial jargon, model training, deployment via APIs, and monitoring for "model drift" in volatile markets.

Requirements

• BA, Master’s or PhD in Computer Science, Data Science, or a related field with a focus on Natural Language Processing or Deep Learning.
• Advanced proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Proven experience with Transformers (BERT, RoBERTa), Large Language Models (LLMs), and vector databases (e.g., Pinecone, Milvus, or Weaviate).
• Strong experience in building data pipelines using tools like Spark, Kafka, or Airflow, and proficiency in SQL.
• Familiarity with financial terminology and the ability to handle domain-specific data challenges (e.g., interpreting ticker symbols vs. common words).
• Experience deploying models in a cloud environment (AWS, GCP, or Azure) using Docker and Kubernetes, ensuring low-latency inference for real-time trading signals.
• Ability to design robust evaluation frameworks for NLP models, moving beyond standard metrics to business-impact metrics like "signal-to-noise ratio" in trading.
• A "builder" mindset with the ability to prototype rapidly and move from a research paper to a production-ready feature in weeks, not months.
• Fluent in English with excellent documentation and cross-team coordination skills.

Start-up Benefits

• Compensation: A highly competitive salary package that recognizes your expertise and contribution.
• Modern Work Culture: Embrace a remote-first environment with flexible working hours, designed to support your work-life harmony.
• Generous Time Off: Annual Leave - 24 days, dedicated paid sick leave, and Public Holidays.
• Professional Evolution: Grow your skills with a dedicated learning budget and clear pathways for accelerated career development.
• Meaningful Impact: Join a world-class team building a prestigious, next-generation modern money platform that is redefining the future of finance.

Key skills/competency

• Natural Language Processing (NLP)
• Deep Learning
• Large Language Models (LLMs)
• Python
• PyTorch/TensorFlow
• MLOps
• Financial Sentiment Analysis
• Fraud Detection
• Data Pipelines
• Cloud Deployment (AWS/GCP/Azure)