Join a high-impact team driving advanced machine learning systems to protect organizations from evolving security threats.
Architect large-scale detection models that power real-time message analysis, shaping the strategic ML direction of a world-class cybersecurity platform.
Work at the intersection of deep learning, behavioral analysis, and system design, leading initiatives that directly defend thousands of global enterprises.
Influence foundational AI infrastructure and collaborate with top engineers across the stack.
Act as a technical leader and domain expert across multiple ML workstreams, mentoring teams and influencing the broader machine learning roadmap.
Architect scalable, generalizable ML systems that address critical detection gaps, guiding model integration across various types—from heuristics to deep learning.
Lead strategic initiatives, including global model training infrastructure and cross-team ML platforms.
Own the full ML lifecycle: data analysis, prototyping, training, production deployment, and performance monitoring.
Troubleshoot complex model behaviors using a deep understanding of theoretical and applied machine learning principles.
Design automated retraining and evaluation pipelines to adapt to evolving threats and ensure detection efficacy.
Collaborate with cross-functional stakeholders to ensure ML solutions are aligned with real-world threats and customer-facing product goals.
Requirements:
8+ years of experience developing impactful, large-scale ML systems in applied environments such as ad tech, fraud detection, or recommendation systems.
Proven expertise in building, deploying, and optimizing ML models that operate in high-throughput, real-time applications.
Deep understanding of machine learning theory, including limitations and edge cases of deep learning models.
Strong hands-on experience across the ML stack: data engineering, model training, experimentation, and deployment.
Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
Demonstrated ability to diagnose and resolve performance issues with complex models.
Bachelor’s degree in Computer Science, Applied Sciences, or related field.
Ability to drive architecture-level ML strategy and propose long-term, scalable technical solutions.
Nice to have: MS or PhD in Computer Science, Electrical Engineering, or a related field, experience with MLOps and scalable data pipeline development, and a track record leading large cross-functional ML initiatives across multiple teams.
Benefits:
Base salary range: $229,500 – $270,000 USD.
Annual performance bonus and equity (RSUs) available for eligible roles.
Comprehensive medical, dental, and vision insurance.
Flexible work hours and remote-first culture.
Paid parental leave and wellness support.
Home office setup allowance.
Career growth opportunities within a fast-scaling, AI-driven organization.
