TL;DR

Lead Machine Learning Engineer (Cybersecurity): Architecting the data-driven strategy for threat detection capabilities, focusing on building and accelerating scalable and resilient machine learning pipelines across the security engineering organization. Focus on advanced probabilistic modeling, graph analytics, supervised and unsupervised learning to expose sophisticated threats that evade traditional defenses, directly reducing the organization's risk surface.

Location: California - San Francisco, California - Palo Alto, New York - New York, Washington - Bellevue

Company

%hirify_global% is the Customer Company, inspiring the future of business with AI+ Data +CRM.

What you will do

Shape the defense strategy by translating vague security threats into concrete mathematical problems and championing a rapid prototyping culture.
Lead the evolution of threat detection by introducing more advanced probabilistic modeling, graph analytics, supervised and unsupervised learning.
Act as a force multiplier, mentoring junior scientists and engineers, and building internal tooling, feature stores, and libraries.
Prioritize engineering rigor (CI/CD, scalable code) and adversarial resilience to deliver production-grade models that the SOC actually trusts.

Requirements

Extensive experience (3-5+ years) in data science, with at least 2+ years dedicated to the cybersecurity domain.
Practical knowledge of security frameworks such as MITRE ATT&CK and OCSF.
Hands-on comfort with high-volume logs and proficiency with Spark/Pyspark, Snowflake, Flink, and streaming services such as Apache Kafka.
Deep understanding of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch).
Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring.

Nice to have

Masters or PhD in a quantitative field.
Expertise in advanced Natural Language Processing (NLP) methodologies.
Experience contributing to open-source security data science tools.
Background in offensive security (Penetration Testing/Red Teaming) with an "attacker's mindset."

Culture & Benefits

Empowerment to be a Trailblazer, driving performance and career growth.
Opportunity to improve the state of the world.
Focus on core values.