nference is an AI-powered technology company transforming biomedical research by leveraging large-scale data, advanced machine learning, and deep scientific expertise. Our mission is to accelerate breakthroughs in healthcare and life sciences by building innovative products that help researchers and clinicians solve complex real-world problems.
Responsibilities
Assist in developing and evaluating machine learning and deep learning models.
Support LLM-based workflows, including prompting, embeddings, and retrieval pipelines.
Perform data preprocessing, feature engineering, and dataset preparation.
Conduct experiments, analyze results, and track model performance.
Build basic model inference components such as REST APIs and batch pipelines.
Contribute to Docker-based workflows and Git version control practices.
Debug and optimize data, training, and inference pipelines.
Document experiments, models, and results for reproducibility.
Follow data privacy and compliance guidelines while handling healthcare data.
Required Skills:
Pursuing or recently completed a Bachelor's/Master's degree in Computer Science, Data Science, AI, or a related field.
Strong programming skills in Python.
Understanding of machine learning, deep learning, and NLP fundamentals.
Familiarity with PyTorch, TensorFlow, or scikit-learn.
Knowledge of transformers, embeddings, and LLM concepts.
Exposure to REST APIs, Git, and Docker.
Strong analytical and problem-solving skills.
Preferred Skills
Academic or personal projects involving ML, NLP, or Generative AI.
Exposure to RAG, vector databases, or LLM applications.
Awareness of healthcare data privacy concepts such as HIPAA.
What You'll Gain
Hands-on experience with cutting-edge AI and healthcare technology.
Mentorship from experienced data scientists and engineers.
Exposure to real-world machine learning and LLM applications.
Opportunity to contribute to impactful healthcare AI solutions.
Note: This internship is intended to provide practical industry experience and does not guarantee full-time employment upon completion.