Senior ML Researcher

As a Senior ML Researcher at Bold, you’ll design and build language models that directly improve how organizations detect and stop threats.

This role is for you if you want to push the frontier of applied ML and small language models in cybersecurity: building models that understand behavior, detect threats, and make high-quality decisions under strict real-world constraints.

Key Responsibilities:

• Lead advanced ML research for endpoint security, with focus on designing and training language models.
• Develop and evaluate new model architectures, feature representations, embeddings, classifiers, ranking methods, and small language model approaches.
• Push the boundaries of detection performance across precision, recall, robustness, generalization, explainability, and operational impact.
• Partner with ML engineers and security researchers to validate research findings and support their transition into production systems.
• Help define Bold’s long-term ML research direction, including model strategy and future AI capabilities.

Qualifications:

• Master's degree in Computer Science, Mathematics, Statistics, or Engineering with 5+ years of relevant experience, or a PhD in a related field with 2+ years of relevant experience.
• 5+ years of experience in machine learning research, applied ML, or a closely related role, with a strong focus on language models.
• At least 2 years of applied Machine Learning experience in an industry setting, with a proven track record of deploying models to production.
• A track record of published papers in relevant ML conferences or journals is highly preferred.
• Strong foundation in ML fundamentals, including supervised learning, representation learning, embeddings, model architectures, optimization, statistical reasoning, and evaluation methodology.
• Proven ability to develop and validate models for complex real-world domains, ideally involving noisy, sparse, imbalanced, or adversarial data.
• Hands-on experience with Python and modern ML research frameworks such as PyTorch, TensorFlow, JAX, scikit-learn, or similar tools.
• Strong understanding of model compression and optimization techniques.
• Strong ownership, collaboration, and communication skills, with the ability to work effectively across disciplines.
• Strong ownership, collaboration and communication skills, with the ability to work effectively across disciplines