Job Description:
We are seeking a skilled Natural Language Processing (NLP) Engineer with a strong foundation in pre-training techniques, state-of-the-art transformer architectures, and advanced post-training tools. This role will focus on developing, refining, and deploying cutting-edge NLP systems, including speech-to-text engines, retrieval-augmented generation (RAG) frameworks, and real-world applications of language models. You will collaborate with a multidisciplinary team to create robust, scalable solutions that address complex natural language challenges.
Responsibilities:
Model Development and Pre-Training: Research and implement advanced pre-training techniques for NLP models, including self-supervised learning and masked language modeling. Fine-tune transformer-based architectures (e.g., BERT, GPT, T5) to achieve state-of-the-art performance on domain-specific tasks.
Post-Training and RAG Implementation: Develop and optimize retrieval-augmented generation pipelines, leveraging tools like LangChain for knowledge integration.
Speech-to-Text Systems: Evaluate and deploy automatic speech recognition (ASR) tools such as Whisper, DeepSpeech, or Kaldi, ensuring high accuracy across diverse audio datasets. Integrate ASR outputs into NLP pipelines for seamless speech-to-text applications.
Model Deployment and Optimization: Collaborate with DevOps teams to deploy NLP models in production environments, ensuring scalability, low latency, and reliability.
Data Processing and Feature Engineering: Build and maintain pipelines for tokenization, text normalization, and feature extraction tailored to specific use cases. Optimize large-scale datasets for efficient model training and inference.Requirements:Educational Background:
Requirements:
Bachelors, Masters, or PhD in Natural Language Processing, Machine Learning, Artificial Intelligence, Data Science, or a related field.Experience:
Minimum 3 years of professional experience in NLP, machine learning, or related fields.Technical Skills:
Expertise in transformer architectures (e.g., BERT, GPT, T5) and pre-training methodologies.
Experience with post-training tools like LangGraph, LangChain, and retrieval-based frameworks (RAG).
Familiarity with ASR engines, such as Whisper, DeepSpeech, Kaldi, or Julius, and their integration into NLP pipelines.
Strong programming skills in Python, with experience in libraries like Hugging Face Transformers, PyTorch, and TensorFlow.
Knowledge of model deployment technologies (e.g., Docker, Kubernetes) and serving frameworks like FastAPIor TorchServe.
Proficiency with data processing tools such as spaCy, NLTK, and OpenAI APIs.Preferred Skills:
Experience with optimizing NLP models for real-time inference and low-latency environments.
Familiarity with vector search engines like FAISS, Pinecone, or Weaviate.
Understanding of domain-specific language tasks such as named entity recognition (NER), text summarization, or sentiment analysis.
Compensation: "Compensation will amount to approximately $15,000 to $25,000 USD for the duration of the contract, estimated to be six months; Could be extended longer".
Job Types: Full-time, Contract
Contract length: 6 months
Work Location: Remote