Bitdefender

Bitdefender is a cybersecurity leader delivering best-in-class threat prevention, detection, and response solutions worldwide. Guardian over millions of consumer, enterprise, and government environments, Bitdefender is one of the industry’s most trusted experts for eliminating threats, protecting privacy, digital identity and data, and enabling cyber resilience. With deep investments in research and development, Bitdefender Labs discovers hundreds of new threats each minute and validates billions of threat queries daily. The company has pioneered breakthrough innovations in antimalware, IoT security, behavioral analytics, and artificial intelligence and its technology is licensed by more than 180 of the world’s most recognized technology brands. Founded in 2001, Bitdefender has customers in 170+ countries with offices around the world. For more information, visit https://www.bitdefender.com

We are seeking a motivated and curious Junior Researcher in Machine Learning for a hands-on research role. You will work closely with our research team on an applied or theoretical ML project with the goal of producing a research paper for submission to a peer-reviewed venue (e.g., workshop, conference, or journal).

This role is an opportunity to:

• Deepen your understanding of machine learning fundamentals;
• Gain experience with state-of-the-art ML frameworks and tools;
• Contribute to a collaborative research project and co-author a publication;
• Develop research skills including literature review, experimentation, and writing;
• You will be supported by a team of experienced ML researchers and engineers.

Responsibilities:

• Participate in weekly research meetings and collaborative brainstorming sessions;
• Conduct literature reviews to support the research direction;
• Implement and evaluate ML models using standard libraries (PyTorch);
• Analyze experimental results and visualize findings;
• Contribute to writing and preparing a research manuscript with the team;
• Identify and explore novel machine learning research directions aligned with our long-term goals;
• Design and run rigorous experiments to validate hypotheses;
• Collaborate with other researchers and engineers to integrate ideas into prototypes;
• Publish findings in top-tier conferences and journals (NeurIPS, ICML, CVPR, etc.);
• Stay current with cutting-edge research and contribute to open-source initiatives or datasets when possible.

Required Qualifications:

• Strong programming skills in Python;
• Solid understanding of ML basics (e.g., supervised learning, overfitting, regularization, model evaluation);
• Familiarity with PyTorch;
• Experience with Jupyter Notebooks and standard data science tools (NumPy, pandas, matplotlib);
• Ability to read and summarize academic papers in machine learning.

Preferred Qualifications:

• Undergraduate or early graduate-level coursework in ML, statistics, or data science;
• Experience with deep learning models and datasets (e.g., CNNs, transformers);
• Previous involvement in a research project, even at the class level;
• Basic familiarity with LaTeX or academic writing tools;
• Strong communication and collaboration skills.