Are you passionate about teaching machines to understand, interpret, and generate human language? Do you thrive on building advanced language models that can extract meaning from vast amounts of text, power conversational agents, and transform natural language data into actionable insights? If you’re excited about shaping the future of human-computer interaction through Natural Language Processing (NLP), then our client has an exciting opportunity for you. We’re looking for an NLP Engineer (aka The Language Architect) to design and deploy NLP models that enhance our products and solve complex linguistic challenges.

As an NLP Engineer at our client, you’ll develop sophisticated language models, work with large-scale datasets, and create solutions that improve text analysis, language generation, and understanding across various applications. Your expertise will power features like chatbots, voice assistants, sentiment analysis, and much more.

Key Responsibilities:

1. Develop NLP Models and Algorithms:
• Design and implement cutting-edge NLP models using machine learning and deep learning techniques. You’ll work with architectures like transformers, recurrent neural networks (RNNs), and bidirectional encoder representations (BERT, GPT) to develop models for text classification, named entity recognition, sentiment analysis, and language generation.
2. Data Preprocessing and Feature Engineering:
• Preprocess and clean large textual datasets, converting unstructured data into structured formats suitable for model training. You’ll apply tokenization, stemming, lemmatization, and other NLP techniques to extract meaningful features from raw text.
3. Train and Fine-Tune Language Models:
• Train, fine-tune, and optimize pre-trained language models like GPT, BERT, and T5 on custom datasets. You’ll experiment with various model architectures and hyperparameters to improve accuracy, speed, and generalization.
4. Deploy NLP Solutions in Production:
• Work with engineering teams to deploy NLP models into production environments. You’ll ensure models are scalable, efficient, and integrated seamlessly into applications such as chatbots, recommendation engines, or voice assistants.
5. Model Evaluation and Performance Monitoring:
• Evaluate model performance using metrics such as accuracy, F1-score, precision, recall, and perplexity. You’ll monitor models in production and retrain or fine-tune them as needed to maintain performance over time.
6. Collaborate with Cross-Functional Teams:
• Work closely with product managers, software engineers, and data scientists to understand business goals and integrate NLP solutions into products. You’ll provide insights on linguistic challenges and propose solutions that align with business objectives.
7. Stay Updated on NLP Research and Trends:
• Keep up with the latest advancements in NLP and deep learning. You’ll experiment with new techniques like zero-shot learning, transfer learning, and transformer-based architectures to bring innovation into our client’s NLP solutions.

Requirements

Required Skills:

• NLP Expertise: Strong knowledge of NLP techniques, including text classification, named entity recognition, sentiment analysis, and machine translation. You’re experienced with pre-trained language models like BERT, GPT, RoBERTa, and T5.
• Programming and Tools: Proficiency in Python and experience with NLP libraries and frameworks like Hugging Face’s Transformers, SpaCy, NLTK, or Stanford NLP. You can implement NLP models and algorithms using deep learning frameworks like TensorFlow or PyTorch.
• Text Preprocessing and Feature Engineering: Expertise in preprocessing raw text data, including tokenization, stemming, lemmatization, and vectorization (TF-IDF, word embeddings). You understand how to handle large-scale text data for NLP tasks.
• Model Deployment: Experience deploying NLP models in production environments using tools such as Docker, Kubernetes, or cloud-based services (AWS, Google Cloud, or Azure). You’re familiar with API development and integration with web or mobile applications.
• Research and Innovation: Strong interest in NLP research, with the ability to stay updated on new models, architectures, and methodologies. You know how to apply cutting-edge research to practical, real-world problems.

Educational Requirements:

• Bachelor’s or Master’s degree in Computer Science, Computational Linguistics, AI, Machine Learning, or a related field. Equivalent experience in NLP engineering is also highly valued.
• Certifications or additional coursework in NLP, machine learning, or AI are a plus.

Experience Requirements:

• 3+ years of experience in NLP engineering, with hands-on experience developing, training, and deploying NLP models in production environments.
• Proven experience working with large-scale textual datasets and applying machine learning or deep learning models to solve NLP challenges.
• Experience with transformer-based models and advanced NLP techniques is highly desirable.

Benefits

• Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
• Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.
• Work-Life Balance: Flexible work schedules and telecommuting options.
• Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.
• Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.
• Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.
• Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.
• Tuition Reimbursement: Financial assistance for continuing education and professional development.
• Community Engagement: Opportunities to participate in community service and volunteer activities.
• Recognition Programs: Employee recognition programs to celebrate achievements and milestones.