Detection Engineer, Protective Services

About the Team DoorDash is one of the world's most trusted on-demand logistics platforms. The Global Safety and Security team exists to keep it that way, protecting our people, property, operations, brand, and reputation across all our markets. We manage risk and create value through agility, technology, and a people-first approach. Our DoorDash, Deliveroo, and Wolt brands now span more than 40 countries, dozens of offices, and tens of thousands of employees. Protective Services focuses specifically on the safety of our executives, their families, and the operations that support them. Its detection engineering function brings together enterprise, marketplace, open-source, and physical security signals to identify threats that do not fit within a traditional SOC boundary. This role builds the detection capabilities that turn those signals into timely, actionable information for Protective Services. About the Role We are hiring a Senior Detection Engineer to build and improve the technical capabilities Protective Services uses to identify threats to protected individuals. You will develop detections, pipelines, enrichment, and automation across enterprise security telemetry, marketplace signals, open-source information, and physical security systems. This detection problem extends beyond conventional cybersecurity alerts. Relevant indicators may appear across identities, endpoints, cloud environments, support interactions, marketplace activity, access-control events, or unstructured online content. You will determine how to combine those signals, distinguish meaningful escalation from noise, and directly investigate resulting activity alongside Protective Services partners. That proximity to active cases grounds engineering decisions in real investigative needs and reveals opportunities to improve fidelity, automate friction, and reduce time to action. This is a hands-on security engineering role. You will work in code, queries, data pipelines, detection repositories,

Adobe is seeking a Machine Learning Engineer to join the Adobe Risk Platform (ARP) team. ARP is Adobe's centralized, adaptive system for detecting, preventing, and mitigating fraud and abuse across products and services — protecting surfaces like Commerce, Stock, and Firefly with real-time risk decisions, without degrading the customer experience. In this position, you will help build and develop machine learning models to identify fraudulent activity, detect abusive account behavior, and protect the experience of hundreds of millions of users. You'll work across the model lifecycle from raw behavioral data and feature engineering, to model training, deployment, and monitoring alongside senior engineers on the team. Key Responsibilities Help build and train ML models covering various fraud and abuse areas. These include financial transaction fraud, device-related deception, and account and identity abuse. The goal is a unified, continuously-updated trust and risk score. Contribute to feature engineering across transaction, device, and behavioral event data. Build and maintain feature pipelines on Databricks and Spark, transforming raw transaction and device event data into high-quality model inputs. Help translate prototypes into production ML systems, working with senior engineers on scalability, reliability, and observability. Support MLOps practices: experiment tracking, model versioning, CI/CD, and production monitoring. Collaborate cross-functionally with data science, product, and platform teams to understand fraud and abuse patterns across Adobe's surfaces. Stay ahead of advances in ML/AI, particularly in fraud detection and behavioral modeling, and bring relevant ideas to the team. Minimum Qualifications Bachelor's degree in Computer Science, Machine Learning, Data Science, or related field (or equivalent experience). 3

Adobe is seeking a Machine Learning Engineer to join the Adobe Risk Platform (ARP) team. ARP is Adobe's centralized, adaptive system for detecting, preventing, and mitigating fraud and abuse across products and services — protecting surfaces like Commerce, Stock, and Firefly with real-time risk decisions, without degrading the customer experience. In this position, you will help build and develop machine learning models to identify fraudulent activity, detect abusive account behavior, and protect the experience of hundreds of millions of users. You'll work across the model lifecycle from raw behavioral data and feature engineering, to model training, deployment, and monitoring alongside senior engineers on the team. Key Responsibilities Help build and train ML models covering various fraud and abuse areas. These include financial transaction fraud, device-related deception, and account and identity abuse. The goal is a unified, continuously-updated trust and risk score. Contribute to feature engineering across transaction, device, and behavioral event data. Build and maintain feature pipelines on Databricks and Spark, transforming raw transaction and device event data into high-quality model inputs. Help translate prototypes into production ML systems, working with senior engineers on scalability, reliability, and observability. Support MLOps practices: experiment tracking, model versioning, CI/CD, and production monitoring. Collaborate cross-functionally with data science, product, and platform teams to understand fraud and abuse patterns across Adobe's surfaces. Stay ahead of advances in ML/AI, particularly in fraud detection and behavioral modeling, and bring relevant ideas to the team. Minimum Qualifications Bachelor's degree in Computer Science, Machine Learning, Data Science, or related field (or equivalent experience). 5

About the Team Security is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity. The Security team protects OpenAI’s technology, people, and products. We are technical in what we build but operational in how we execute, and we support every product and research effort at OpenAI. Our tenets include prioritizing for impact, enabling researchers and developers, preparing for future transformative technologies, and fostering a strong, collaborative security culture. About the Role OpenAI is seeking a Security Software Engineer to join the Infrastructure Security (InfraSec) team. InfraSec safeguards the core of OpenAI’s research and production environments—GPU supercomputing clusters, multi-cloud infrastructure, datacenters, networking, storage, and the critical services that power our frontier AI models. Our charter spans everything from bare-metal hardware and firmware to Kubernetes clusters, service meshes, and the data pathways that carry highly sensitive model weights and user data. As a Security Software Engineer, you will design and build critical foundational services, such as authentication systems, egress/ingress proxies, access brokers, and key management platforms, that demand high standards of reliability, scalability, and software craftsmanship. These systems form the security backbone of OpenAI’s supercomputing environment and must remain robust under intense scale and adversarial pressure. In this role, you will: Architect and implement production-grade security services (e.g., auth services, access brokers, secure proxies, key-management infrastructure) that provide strong guarantees across hardware, operating systems, Kubernetes, networks, and CI/CD. Partner with infrastructure and research engineers to embed security into high-performance compute clusters, enabling rapid model training and deployment without compromising protection. Develop automation and detection tooling to continuously identif

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We are hiring a Principal Software Engineer for our Anti-Abuse team. The Anti-Abuse team sits at the core of protecting Snowflake and our customers from abuse on the Snowflake Platform. We strive to see the complete picture, leading platform-wide abuse and fraud detection while designing and deploying proactive controls for Snowflake's highest-risk capabilities: Snowpark, Data Sharing, Cortex AI, and the next generation of cloud engineering challenges as Snowflake rapidly scales. In this role, you will be the lead technical architect responsible for defining and driving our technical strategy for abuse prevention, detection, and response across the entire Snowflake ecosystem. AS A PRINCIPAL SOFTWARE ENGINEER, ANTI-ABUSE AT SNOWFLAKE, YOU WILL: Define and drive the long-term technical vision for abuse and fraud detection across the Snowflake platform, serving as the chief architect for platform-wide anti-abuse strategy Partner directly with product and engineering teams building Snowflake's newest and highest-risk surfaces (Snowpark user code execution runtimes, Data Sharing and Marketplace, AI/LLM services) to design and deploy robust anti-abuse controls from day one Architect and scale high-throughput, low-latency telemetry pipelines and risk-scoring engines capable of ana

CSQ227R165 At Databricks, we are passionate about empowering data teams to tackle the world’s most challenging problems — from bringing the next mode of transportation to reality to accelerating the development of medical breakthroughs. We achieve this by building and operating the world’s best data and AI infrastructure platform, enabling our customers to leverage deep data insights and enhance their business. Founded by engineers — and customer-obsessed — we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. We are seeking an experienced Tech Lead to shape the future of platform observability and proactive monitoring. This is a high-impact technical role for someone who thrives at the intersection of platform reliability, incident response, and customer obsession.You will serve as a critical first responder for the Databricks Platform, leading complex investigations, designing observability solutions, and driving systemic improvements that enhance customer experience and platform stability. The successful candidate will be responsible for developing monitoring solutions, alerting mechanisms, and customer-focused incident detection tools. They will also work closely with cross-functional engineering teams to investigate and resolve incidents, perform root cause analyses, and propose solutions to improve the reliability and stability of the Databricks Platform. The impact you will have: Lead platform incident investigation, coordinating cross-functional teams through rapid detection, mitigation, and resolution to minimize customer impact. Conduct thorough post-incident root cause analysis across infrastructure, services, and cloud providers to identify systemic patterns and prevent future occurrences. Design and implement customer-focused alerting pipelines and end-to-end observabil