Junior NLP Engineer

We are a pioneering technology firm at the forefront of applied artificial intelligence. Our mission is to solve complex, real-world problems by building intelligent systems that redefine industry standards across finance, healthcare, and e-commerce. You will join a culture of curiosity, continuous learning, and collaborative innovation, where your contributions directly impact products used by millions. We operate on a results-oriented basis, empowering our team members with the autonomy to explore and implement groundbreaking solutions.

What You'll Do

- Develop and implement sophisticated text processing algorithms using Python and core NLP libraries like spaCy and NLTK.

- Train, fine-tune, and evaluate machine learning and deep learning models—including transformers like BERT—for tasks such as text classification, sentiment analysis, and named entity recognition.

- Collaborate with data scientists and software engineers to integrate NLP models into larger applications and production pipelines.

- Preprocess and manage large-scale text datasets, ensuring data quality and readiness for model training.

- Debug and troubleshoot model performance issues, meticulously documenting your findings and solutions for the team.

- Assist in the deployment of NLP solutions, potentially using containerization technologies (like Docker) and cloud services.

- Stay current with the latest advancements in the NLP and large language model (LLM) space... and bring new ideas to the team.

- Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

What You'll Bring

- A Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field.

- Solid programming proficiency in Python and experience with its data science ecosystem (e.g., Pandas, NumPy).

- Foundational knowledge of Natural Language Processing concepts and hands-on experience with NLP libraries such as spaCy, NLTK, or Hugging Face Transformers.

- Familiarity with machine learning principles and experience with at least one major deep learning framework like TensorFlow or PyTorch.

- A strong analytical and problem-solving mindset, with the ability to debug complex systems with precision.

- Excellent communication and teamwork skills, with a genuine desire to collaborate and learn in a fast-paced setting.

- An understanding of version control systems, particularly Git, for collaborative development.

- Adaptability and eagerness to tackle new challenges in the rapidly evolving field of AI.

Bonus Points

- Experience with cloud platforms (AWS, GCP, or Azure) and their associated machine learning services.

- Familiarity with containerization tools like Docker.

- Contributions to open-source projects or a portfolio of personal NLP projects.

- Basic understanding of API development (e.g., using FastAPI or Flask).