UMO

Machine Learning Engineer, NLP

UMO

full-time

Posted on:

Location Type: Hybrid

Location: Lisbon • Portugal

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Tech Stack

AirflowAWSAzureCloudDockerGoogle Cloud PlatformKafkaKubernetesPythonPyTorchSparkSQLTensorflow

About the role

  • Build and deploy NLP models to analyze news, social media (Twitter/X, Discord), and Reddit to gauge market sentiment for stocks and crypto assets.
  • Develop systems to identify and extract entities (tickers, company names, wallet addresses, transaction IDs) from unstructured financial documents and chat logs.
  • Create pipelines to parse and extract data from financial statements, whitepapers, and regulatory filings (e.g., SEC filings) to assist in automated research.
  • Implement NLP techniques to analyze transaction metadata and communication patterns to identify potential money laundering (AML) or fraudulent payment activity.
  • 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.
  • Optimize internal search engines using semantic search and embeddings to help users find relevant financial instruments or transaction history.
  • 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.
Benefits
  • A highly competitive salary package that recognizes your expertise and contribution.
  • Embrace a remote-first environment with flexible working hours, designed to support your work-life harmony.
  • Annual Leave- 24 days, dedicated paid sick leave, and Public Holidays.
  • Grow your skills with a dedicated learning budget and clear pathways for accelerated career development.
  • Join a world-class team building a prestigious, next-generation modern money platform that is redefining the future of finance.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
Natural Language ProcessingDeep LearningPythonPyTorchTensorFlowTransformersLarge Language ModelsSQLData PipelinesModel Evaluation
Soft Skills
Builder mindsetRapid prototypingDocumentationCross-team coordination