NLP Engineer
18 jobs found
| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
Hermeneutic Investments | Remote | $100k - $150k | |||
Web 3 Ventures | Remote |
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Binance | Taipei, Taiwan |
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CEF AI | San Francisco, CA, United States | $150k - $180k | |||
| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
Genies | San Mateo, Portugal | $237k | |||
genies | San Mateo, Portugal | $112k - $120k | |||
Binance | Taipei, Taiwan |
| |||
Genies, Inc. | San Mateo, CA, United States | $112k - $120k | |||
Binance | Taipei, Taiwan |
| |||
Openmesh | Sydney, Australia | $90k - $150k | |||
Genies, Inc. | Remote | $96k - $115k | |||
Binance | Asia |
| |||
Menyala | Singapore, Singapore | $11k - $75k | |||
Menyala | Singapore, Singapore | $63k - $90k | |||
Aldrin | Remote | $90k - $100k |
About Us / Why Join?
We are a rapidly growing crypto hedge fund, 2 years old, managing a 9-figure AUM, generating 200%+ annualized returns with a 4 Sharpe.
Our team has grown to approximately 40 professionals across Trading & Research, Technology, and Operations.
About the Role
We are hiring a AI/NLP Engineer to lead the development of cutting-edge NLP systems that support our real-time trading decision engine and accompanying internal tools. This role involves working on large-scale datasets to extract actionable insights and enhance the performance of our trading strategies.
As part of our technology team, you will design and implement robust, scalable NLP pipelines and adaptive learning systems. You will collaborate closely with researchers, and engineers to drive innovation in analyzing unscheduled market news.
Responsibilities
- Lead the development and optimization of our NLP-based decision engine.
- Build and maintain ML models for news classification and sentiment analysis.
- Enhance semantic pattern matching and false positive detection systems.
- Develop feedback loops for continuous improvement of NLP models.
- Integrate NLP capabilities with search engines like Elasticsearch.
- Design adaptive learning systems to improve real-time decision-making.
- Work with stakeholders to implement NLP-driven insights into trading pipelines.
Requirements
Must Haves
- 7+ years of experience in NLP, machine learning, or related roles.
- Expert knowledge of NLP frameworks and tools, including spaCy and transformers.
- Hands-on experience with BERT and other transformer architectures.
- Proficiency in Python and deep learning libraries like PyTorch or TensorFlow.
- Strong background in text preprocessing, semantic similarity, and text classification.
- Experience with sentiment analysis, Named Entity Recognition (NER), and topic modeling.
- Proven ability to deploy, monitor, and optimize ML models in production.
- Excellent problem-solving skills and ability to handle large datasets (structured or unstructured).
Nice to Have
- Knowledge of Elasticsearch NLP integration and semantic search.
- Experience with reinforcement learning and online learning systems.
- Proficiency in CI/CD for ML, Docker, and microservices architecture.
- Experience with GPU optimization and model quantization.
- Background in multilingual NLP and domain-specific adaptation.
- Familiarity with financial or market-related NLP tasks.
What does NLP Engineer do?
A Natural Language Processing (NLP) Engineer is a professional who designs and develops software systems that can understand and generate human language
They typically work with machine learning and artificial intelligence (AI) technologies to create NLP solutions for various applications, including chatbots, virtual assistants, search engines, sentiment analysis, and language translation
NLP engineers play a critical role in developing software systems that can understand and generate human language, enabling us to interact with technology in more natural and intuitive ways
The role of an NLP Engineer includes:
- Data preprocessing: NLP engineers are responsible for cleaning, normalizing, and transforming raw text data into a format that can be used by machine learning algorithms.
- Feature engineering: NLP engineers design and develop features that are relevant to the NLP problem they are trying to solve. They extract features such as word frequency, part-of-speech tagging, and sentiment analysis.
- Building machine learning models: NLP engineers use machine learning algorithms such as deep learning, neural networks, and decision trees to train models that can understand and generate human language.
- Evaluating and improving models: NLP engineers evaluate the performance of their models using various metrics such as accuracy, recall, and F1 score. They also use techniques such as cross-validation and hyperparameter tuning to improve model performance.
- Integration: NLP engineers integrate their NLP solutions into larger software systems or platforms, such as chatbots or virtual assistants.