Machine Learning Application Engineer to join the Engineering team, sitting at the intersection of data engineering and applied ML, taking existing model types and putting them to work inside product and data pipelines.
Locations (4)
North America
Europe
Middle East
APAC
Compensation
Salary: $190,000
Competitive Equity Package
Comprehensive Benefits Plan
Responsibilities
Apply clustering, classification, anomaly detection, and other established ML techniques to cybersecurity data problems in the ICS/OT domain
Integrate ML model outputs into existing data pipelines and product workflows, supporting both batch and near-real-time processing patterns
Owner of analytics infrastructure and datasets that directly inform operational decisions across Zipline, ensuring data accuracy, freshness, and incident response.
Compensation
The starting cash range for this role is $155,000 - $210,000.
Responsibilities
Own end-to-end analytics products: define success metrics, design schemas, implement ETL/ELT pipelines, test for accuracy, and operate datasets in production.
Deliver the first 6-month roadmap items as DRI, including consolidating multi-source aircraft availability signals into a single fleet health data set.
Implement rigorous validation: automated data-quality checks, anomaly detection, and rollback procedures with measurable alert thresholds and agreed remediation SLAs.
Build semantic layers, governed metrics, and documented data contracts consumed by BI and AI tools.
Partner closely with Software, Hardware, Field Ops, and Manufacturing to fix upstream data quality issues at the source.
Requirements
Bachelor's degree in a quantitative field or equivalent experience
Conduct investigations into fraud, abuse, and security incidents within U.S. Government cloud environments.
Compensation
The typical base pay range for this role across the U.S. is USD $102,100 - $202,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $133,800 - $219,200 per year.
Responsibilities
Conduct deep-dive investigations into fraud, abuse, and security incidents
Correlate signals across identity, billing, telemetry, and cross-tenant activity
Differentiate fraud vs. compromise vs. legitimate activity
Own or support Sev-1 / Sev-2 incidents
Execute containment actions
Requirements
Master's Degree in Statistics, Mathematics, Computer Science, or related field
Bachelor's Degree in Statistics, Mathematics, Computer Science, or related field
Nice to have: Experience supporting Azure Government, GCC High, or DoD environments
Nice to have: Knowledge of CJIS Security Policy
Nice to have: Background in threat hunting, digital forensics, or advanced incident response
Lead and support evidence-driven infrastructure response, identity recovery and technical hardening workstreams across enterprise on-premises and cloud environments.
Compensation
Security Research IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Responsibilities
Lead security research across the threat landscape, using multiple data sources to identify vulnerabilities, track attackers, and guide priorities.
Strengthen security data quality and access while maintaining privacy standards and improving analysis through tools and AI.
Direct incident response and large-scale security reviews, identifying root causes, closing protection gaps, and keeping stakeholders informed.
Develop cross-functional solutions to prevent, detect, and disrupt attacks, incorporating customer feedback into mitigations and security practices.
Set infrastructure recovery objectives, technical priorities and evidence requirements for identity, directory, privileged access and enterprise infrastructure workstreams.
Requirements
Doctorate in Statistics, Mathematics, Computer Science, Computer Security, or related field
Master's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field
Bachelor's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field
Lead the enterprise AIOps function responsible for applying artificial intelligence, data science, automation, and reliability engineering capabilities across applications and infrastructure.
Compensation
Compensation range: The salary range for this position is: $143,320- $257,970.
Responsibilities
Define and execute an enterprise AIOps strategy, roadmap, operating model, and investment priorities aligned with technology and business objectives.
Partner across Application Engineering, Product, SRE, Infrastructure, Cloud, Platform, Network, and Operations teams to identify and deliver AI-driven capabilities that improve reliability, availability, resiliency, and customer experience.
Lead the prioritization, development, and adoption of application- and infrastructure-focused use cases, including anomaly detection, root cause analysis, predictive insights and operations, automated remediation, intelligent orchestration, operational automation, and self-healing capabilities.
Establish and mature AI Reliability Engineering (AI SRE) practices so AI-enabled capabilities are secure, governed, measurable, resilient, scalable, and production-ready.
Drive organizational transformation and adoption of AI-enabled operations across the application portfolio and core infrastructure services, including cloud, network, compute, storage, and platform environments.
Lead complex AI/ML initiatives, design, develop, evaluate, and operationalize enterprise AI solutions, mentor junior data scientists, and collaborate across engineering and product teams.
Responsibilities
Design and develop AI/ML solutions for enterprise applications
Evaluate and operationalize models, including RAG and fine-tuning
Lead experimentation and MLOps pipelines
Mentor junior and mid-level data scientists
Collaborate with AI Engineering, Platform Engineering, and product teams
Requirements
Bachelor’s degree in Machine Learning, Statistics, Data Science, Physics, Computer Science, or related field
Responsible for independently executing advanced quality data analytics and investigation activities with minimal management oversight.
Lead cross-functional data-driven initiatives focused on quality improvement, risk reduction, process optimization, and regulatory compliance.
Analyze large and complex datasets from CAPA, Complaints, Nonconformances, Supplier Quality, Manufacturing, and Post-Market Surveillance systems to identify trends, signals, and root causes.
Develop innovative, practical, and compliant analytical solutions to address complex quality and operational challenges.
Apply statistical methods, machine learning models, and predictive analytics to proactively detect emerging quality issues and support decision-making.
Requirements
Bachelors Degree in life sciences or engineering or an equivalent combination of education and work experience
The base compensation range for the role in any of those locations is $117,700 USD - $218,600 USD. For any eligible US locations, unless otherwise noted, the base compensation range for this role is $102,200 USD - $189,800 USD.
Responsibilities
Collect, integrate, clean, verify, resolve, and maintain large and complex datasets.
Analyze data using statistical methods, graph networks, anomaly detection, spatial analysis, pattern-of-life analysis, sentiment analysis, and related techniques.
Develop and apply data models, including risk models, community detection, classification, computer vision, machine learning, and artificial intelligence models.
Translate data findings into actionable insights through dashboards, visualizations, briefings, and clear explanations for technical and non-technical audiences.
Write, edit, customize, and execute Python code to support mission requirements.
Requirements
Bachelor’s or Master’s degree in a technical field such as data science, statistics, computer science, mathematics, physical or biological sciences, geographic information systems, or a related discipline
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