Binance

Machine Learning Engineer, NLP

Binance

full-time

Posted on:

Location Type: Remote

Location: Remote • 🇹🇼 Taiwan

Visit company website
✨ AI Apply
Apply

Job Level

Mid-LevelSenior

Tech Stack

ApacheCloudHadoopJavaKafkaPythonPyTorchScikit-LearnSparkTensorflow

About the role

  • Apply NLP techniques to preprocess and analyze large-scale textual data, developing and fine-tuning Large Language Models (LLMs) and multimodal models to generate actionable business insights.
  • Design, build, and maintain end-to-end machine learning pipelines—including data ingestion, cleaning, feature engineering, model training, evaluation, deployment, and monitoring
  • Lead the deployment of ML models in production environments with a focus on scalability, reliability, availability, and low-latency inference, leveraging cloud infrastructure for optimal performance.
  • Collaborate with business and technical stakeholders to identify AI opportunities, align initiatives with organizational goals, and communicate insights effectively through data analysis and visualization.
  • Stay abreast of the latest AI advancements, particularly in multimodal AI, to continuously integrate cutting-edge technologies into solutions.
  • Explore the use of agentic AI to automate detection and monitoring within risk management systems, improving accuracy and response times.

Requirements

  • Minimum 4 years of industry experience in AI/ML, preferably focused on NLP and/or multimodal AI, with a Master’s degree or higher in Computer Science, Data Science, or related fields.
  • Proficient in big data technologies (e.g., Apache Spark, Hadoop, Kafka, VectorDB) or equivalent platforms.
  • Skilled in programming languages such as Python or Java, with hands-on experience in ML/NLP libraries and deep learning frameworks (TensorFlow, PyTorch, Scikit-learn, SpaCy, NLTK).
  • Strong understanding of modern machine learning and deep learning techniques, including transformer architectures (BERT, GPT), hyperparameter optimization, and methods for handling imbalanced datasets.
  • Experience optimizing and deploying ML models for low-latency inference in production, familiar with end-to-end ML deployment processes including version control (Git), continuous integration/continuous deployment (CI/CD), and managing multiple environments (dev, QA, staging, production).
  • Experience with productionising agentic AI systems or similar autonomous AI solutions is a plus.
  • Prior experience in e-commerce or technology sectors is highly desirable.
Benefits
  • Competitive salary and company benefits
  • Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)

Applicant Tracking System Keywords

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

Hard skills
NLP techniquesLarge Language Modelsmachine learning pipelinesdata ingestionfeature engineeringmodel trainingmodel evaluationmodel deploymentlow-latency inferencehyperparameter optimization
Soft skills
collaborationcommunicationstakeholder engagementdata analysisvisualization
Certifications
Master’s degree in Computer ScienceMaster’s degree in Data Science