Machine Learning Engineer

Accountabilities

• Design, train, fine-tune, and evaluate machine learning models for security detection use cases.
• Build and deploy lightweight, high-performance models optimized for low latency, high throughput, low inference cost, and operational reliability.
• Develop and maintain fine-tuning pipelines for large language models and smaller transformer-based architectures.
• Experiment with advanced model optimization techniques, including knowledge distillation, quantization, pruning, retrieval-augmented generation, and parameter-efficient fine-tuning approaches such as LoRA and adapters.
• Improve detection quality while balancing false positives and false negatives to deliver accurate and reliable security outcomes.
• Build scalable machine learning infrastructure and production-grade inference pipelines.
• Partner with security researchers to translate detection logic and research concepts into ML-powered production systems.
• Measure model performance across key dimensions including quality, inference speed, memory footprint, scalability, and cost.
• Contribute to data engineering, dataset preparation, and labeling workflows supporting supervised machine learning.
• Monitor deployed models and identify opportunities to improve robustness, reliability, and performance over time.
• Develop experimentation and evaluation processes that enable informed decisions about model performance and production readiness.
• Collaborate with platform and product engineering teams to integrate ML capabilities into scalable production environments.
• Apply strong software engineering practices throughout model development, deployment, monitoring, and maintenance.

Requirements

• At least 2 years of experience in Machine Learning Engineering, Applied AI, or a closely related field.
• Demonstrated experience building, deploying, and maintaining machine learning systems in production environments.
• Hands-on experience fine-tuning transformer-based models and/or large language models.
• Strong Python programming and software engineering skills.
• Experience with modern machine learning frameworks such as PyTorch and Hugging Face; TensorFlow experience is an advantage.
• Practical knowledge of model optimization techniques and experience improving inference efficiency at scale.
• Solid understanding of machine learning model evaluation, experimentation, and performance measurement.
• Experience working with data pipelines and datasets used for supervised machine learning.
• Understanding of distributed training and scalable machine learning infrastructure.
• Experience deploying ML models in cloud-based or containerized environments.
• Strong understanding of production software engineering principles, including reliability, scalability, maintainability, and monitoring.
• Analytical and problem-solving mindset, with the ability to investigate complex technical challenges and turn experimentation into practical solutions.
• Ability to collaborate effectively with security researchers, engineers, product teams, and other technical stakeholders.
• Comfortable working in an environment where priorities evolve quickly and continuous experimentation and improvement are encouraged.
• Strong ownership, curiosity, attention to detail, and commitment to delivering reliable production systems.

Benefits

• Competitive compensation.
• Comprehensive employee benefits.
• Flexible work environment.
• Flexible time-off programs.
• Well-being initiatives, including paid wellbeing days.
• Paid volunteer/community outreach days.
• Opportunities for professional development and long-term career growth.
• Global collaboration and networking opportunities with multidisciplinary teams.
• Opportunity to work on advanced machine learning and AI applications with real-world cybersecurity impact.
• Exposure to modern ML technologies, including LLMs, transformers, model optimization, and scalable inference.
• Opportunity to collaborate with experienced machine learning, security research, platform, and product professionals.
• Three-week Work from Anywhere option, subject to applicable policies and eligibility.
• Additional benefits and perks may vary based on location and applicable employment policies.