About the Role
This role is central to applied machine learning and AI innovation, focusing on designing, building, and deploying advanced models to address real-world cybersecurity and identity security challenges. You will collaborate across research, engineering, and product teams to transform complex, high-volume data into production-ready intelligent systems that deliver measurable business impact. The position demands strong expertise in machine learning, statistical modeling, and large-scale data pipelines, with an emphasis on creating robust and scalable solutions. You will also play a crucial role in evaluating and fine-tuning large language models for domain-specific applications, contributing to the future of AI-driven security capabilities. Beyond model development, you will contribute to experimentation design, performance evaluation, and monitoring frameworks to ensure long-term reliability and fairness. This is a highly collaborative and impactful role within a fast-paced environment dedicated to building secure, intelligent systems at scale.
Accountabilities
- Design, develop, and deploy machine learning models (supervised, unsupervised, and deep learning) for production-grade use cases.
- Define experimentation strategies, success metrics, and evaluation frameworks including A/B testing, holdouts, and causal analysis.
- Fine-tune, evaluate, and optimize large language models for domain-specific applications and intelligent automation use cases.
- Collaborate with data engineering teams to design and build scalable data pipelines, feature sets, and training datasets.
- Partner with engineering, product, and business stakeholders to translate ambiguous problems into structured modeling solutions.
- Build and maintain model monitoring systems to track performance, drift, bias, and long-term reliability in production.
- Communicate insights, findings, and recommendations through clear reports, dashboards, and data visualizations for technical and non-technical audiences.
- Mentor other data scientists and contribute to improving modeling standards, best practices, and technical rigor across the team.
Requirements
- Master’s or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience.
- 3+ years of experience building and deploying machine learning models in production environments.
- Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.
- Solid understanding of statistical modeling, experimental design, and evaluation methodologies.
- Experience working with cloud platforms such as AWS, GCP, or Azure for model training and deployment.
- Hands-on experience with LLM fine-tuning techniques (e.g., LoRA, RLHF, instruction tuning) and model serving systems.
- Familiarity with AI-assisted development tools such as GitHub Copilot or similar coding assistants.
- Strong communication skills with the ability to explain complex technical concepts to diverse stakeholders.
- Nice to have: experience in cybersecurity, identity security, anomaly detection, or AI safety research.
Benefits
- Competitive compensation aligned with experience and market benchmarks.
- Comprehensive health, dental, and vision insurance coverage.
- Flexible work arrangements supporting remote collaboration and work-life balance.
- Generous paid time off and parental leave policies.
- Professional development opportunities and access to cutting-edge AI and ML tools.
- Inclusive, collaborative culture focused on learning, innovation, and continuous improvement.
- Opportunity to work on high-impact cybersecurity and identity protection solutions used globally.