AI Research Intern

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.

1. Lead Model Development: Take ownership of developing, training, testing, and optimizing sophisticated machine learning and learning models (e.g., Generative AI, Computer Vision, NLP, Predictive Analytics).
2. Research & Innovation: Conduct independent research on AI trends, techniques, and new technologies, translating innovative academic findings into practical solutions.
3. Data Pipelining: Architect robust data pipelines for large-scale data collection, cleaning, preprocessing, and feature engineering to ensure high-quality inputs for models.
4. Performance & Evaluation: Rigorously evaluate model performance using advanced statistical methods and address potential inaccuracies or issues in AI systems.
5. Teamwork & Integration: Work within agile, cross-functional teams to integrate AI solutions into production environments, presenting sophisticated technical concepts to both technical and non-technical stakeholders.
6. Documentation & Reporting: Maintain meticulous documentation of experiments, methodologies, and results, preparing findings for internal reports and potential external publications.

1. Academic Excellence: Currently pursuing a Master's or Ph.D. in Computer Science, Applied Mathematics, Data Science, Statistics, or a related technical field from a top-tier educational institution.
2. Proven Capability: A strong portfolio (e.g., GitHub, academic papers, project reports) demonstrating hands-on experience in building and deploying AI/ML models with measurable results.
3. Deep Technical Expertise:Proficiency in Python is crucial, with experience in deep learning frameworks like PyTorch or TensorFlow.
4. Strong background in advanced mathematical and statistical concepts, including linear algebra, calculus, and probability theory. Experience with data manipulation libraries (Pandas, NumPy) and an understanding of MLOps principles. Familiarity with cloud platforms (AWS, Azure, GCP) is a plus.
5. Research-Minded: A track record of analytical rigor, problem-solving prowess, and an inquisitive nature that drives innovative thinking.
6. Communication: Excellent verbal and written communication skills, with the ability to clearly articulate technical findings and collaborate effectively in a team environment.