griddable.io · 4 hours ago
Lead Machine Learning Engineer
San Francisco, CA
Full-time
Onsite
Senior Level, Lead/Staff
3+ years expGriddable.io is a foundation machine learning platform team within the Trust Intelligence Platform organization, focusing on building scalable and resilient machine learning pipelines in cybersecurity. The role involves leading threat detection evolution, mentoring junior scientists, and operationalizing intelligence to enhance organizational security.
AnalyticsBig DataCloud Data ServicesData IntegrationInformation TechnologySaaSSoftware
Responsibilities
Shape the Defense Strategy: You will own the decision-making process—translating vague security threats into concrete mathematical problems. By championing a rapid prototyping culture, you will validate hypotheses in days rather than months, ensuring our engineering resources are focused only on high-value detections while killing low-signal ideas early
Detect the "Unknown Unknowns": You will lead the evolution of our threat detection, introducing more advanced probabilistic modeling, graph analytics, supervised and unsupervised learing. Your work will expose sophisticated threats—such as active system intrusions, lateral movement, beaconing, and insider attacks—that evade traditional defenses, directly reducing the organization's risk surface
Elevate the Organization: You will act as a force multiplier, mentoring junior scientists and engineers, and building the internal tooling, feature stores, and libraries that make the whole team faster. You will influence the broader security engineering roadmap to ensure a closed loop security telemetry that is treated as a first-class citizen
Operationalize Intelligence: By prioritizing engineering rigor (CI/CD, scalable code) and adversarial resilience, you will deliver production-grade models that the SOC actually trusts—minimizing "alert fatigue" and maximizing analyst efficiency
Qualification
Required
Extensive experience (3-5+ years) in data science, with at least 2+ years dedicated to the cybersecurity domain designing, implementing and deploying systems of anomaly detection, clustering, and graph models in production
Extended practical knowledge and familiarity with 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 and application 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) and adherence to software engineering best practices
Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring
Solid foundation in feature engineering techniques and the implementation of feature stores
Experience in formulating ML governance policies and ensuring adherence to data security regulations
Ability to explain complex statistical concepts to non-technical stakeholders and executive leadership
Proven ability to manage scope, timelines, and stakeholder expectations across multiple organizations
High degree of autonomy with the ability to look at a vague business problem and structure a data-driven solution without needing a predefined roadmap
A related technical degree is required
Preferred
Masters or PhD in a quantitative field
Expertise in advanced Natural Language Processing (NLP) methodologies
Experience contributing to open-source security data science tools
Presentations at major security conferences (Black Hat, DEF CON, BSides) or data conferences
Background in offensive security (Penetration Testing/Red Teaming) with an 'attacker's mindset.'
Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams
Previous experience in a mentoring role for junior engineers
Track record of publications and/or patents in quantitative disciplines
Company
griddable.io
Griddable.io is a San Jose, CA based SaaS startup that closed Series A funding in 2017 from August Capital, Artiman Ventures, and Carsten Thoma, founding CEO of Hybris (acquired by SAP).
11-50 employees
Funding
Current Stage
Early StageTotal Funding
$8MKey Investors
Artiman Ventures,August Capital
2019-01-28Acquired
2018-02-28Series A· $8M
Leadership Team
Burton Hipp
VP of Engineering/Founder
Company data provided by crunchbase