Detection And Response Jobs

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

About Supabase Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth. About this role We’re hiring experienced performance engineers to make performance measurement at Supabase rigorous, repeatable, and actionable by product teams. This role owns the benchmarking and profiling systems we use to detect regressions, explain performance changes, and publish credible numbers for customers and the market. You’ll build the tooling and the methodology and make it easy for product teams to use it correctly. You’d join a new performance team as one of its first members, working alongside a deeply seasoned performance engineer, with the team growing to roughly six this year. There’s no legacy process to inherit — you’ll help define how we measure and communicate performance metrics at Supabase. What you’ll do Build and evolve benchmarking, profiling, and load-testing tooling (cloud, database, and end-to-end). Define benchmark suites and reference workloads that reflect real customer behavior (and evolve them as the products change). Establish performance baselines and regression gates across products (variance tracking, environment control, and detection of changes). Build mechanisms that turn benchmark results into action: triage workflows, owner routing, and clear, reproducible reports. Partner closely with database teams (e.g. Multigres, OrioleDB) to identify bottlenecks and land concrete performance improvements. Enable the company to publish credible, repeatable performance numbers for customers and the market. Help teams self-serve performance testing and make performance a first-class engineering concern. Problems you might work on Build our continuous benchmarking infrastructure so product teams can spot performance regressions early. Ideally, teams can run benchma

Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform. We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler. Role We are looking for a Staff Detection Engineer to join our team. This is a Hybrid role, reporting to the Senior Manager in the Threat Hunting department. You will analyze emerging threats, adversarial behaviors, and tactics, techniques, and procedures (TTPs) to understand attacker methodologies and improve detection capabilities across our global ecosystem. This role requires conducting proactive and retroactive threat hunting using Zscaler telemetry while working flexible schedules, including weekend night shifts, to meet global operational demands. What you’ll do (Role Expectations) Pick the handful of numbers that tell you the area is healthy (fidelity, false-positive and false-negative rates, ATT&CK coverage, detector regressions) and do the work to move them in the right direction Take what threat intel, hunts, and the analysts consuming your alerts tell you and turn it into what gets fixed and built next Lead

RDQ426R282 Databricks is building the world's best and most secure platform for data and AI. We innovate and deploy industry-leading solutions in security, compliance, and governance. As a member of the Trust and Safety Data Science team, you will work on projects critical to ensuring the security and compliance of the Databricks Platform. Our customers depend on Databricks to keep their data safe, all while orchestrating millions of virtual machines across three clouds in dozens of regions around the globe. Our engineering teams build highly technical products that fulfill real, important needs in the world. We always push the boundaries of data and AI technology, while simultaneously operating with the security and scale that is critical to making customers successful on our platform. We serve many companies with varying security and compliance needs. To efficiently serve these markets, we need to understand how customers use our existing features. This requires data-driven analysis of all aspects of security programs at Databricks. Customers also trust Databricks with their most valuable data and we have the mission to build the most trusted data analytics and ML platform in the world. We’re looking to expand our Trust and Safety Data Science team. You will join a group of “full stack” data scientists who partner with engineering and security teams, focusing on strategic plans that make Databricks secure and safe for our customers. The team will use statistical and machine learning techniques for fraud and abuse detection on our platforms using state of the art methods . You can read more about some of our efforts in this blog post . The work in fraud and abuse detection is dynamic and essential, offering an opportunity to make a substantial impact in maintaining the security and efficiency of business operations. More information is available at https://www.databricks.com/trust . The impact you will have: You will develop and implement Machine Learning mod

Webflow is the agentic web marketing platform for modern marketing teams, helping organizations build, manage, and optimize high-performing web experiences that drive predictable growth and strengthen brand trust. Building at Webflow will need grit, because we move fast, without ever sacrificing craft or quality. About the Role We’re looking for a S enior Infrastructure Engineer to improve reliability and stability of Webflow’s customer-facing, production infrastructure, serving millions of page views per hour. Our product is used by over 2 million users world-wide across 190 countries, and you’ll help ensure our platform is secure and scalable for these users as tens of thousands of projects are launched on Webflow each month. Location: Remote-first (Argentina) Permanent, full-time employee (this is not a contractor position) Application Information: Application deadline: applications accepted on an ongoing basis until position is closed and filled This posting is for a new position. As an Senior Infrastructure Engineer you will: Own and evolve the cloud platform that Webflow's product and engineering teams depend on, including our compute layer, EKS fleet, serverless infrastructure, networking, and cloud operations across AWS and GCP. Help design and maintain our infrastructure-as-code foundation, evolving the patterns and shared components other teams build on. Design and maintain the networking layer that connects Webflow's services, ensuring reliability, security, and scalability across our cloud environments. Help make observability data the foundation of how we operate, improving the dashboards, alerts, and SLOs for the infrastructure you own so that issues are caught before customers notice, and on-call pages are actionable instead of noisy. Build and maintain AI-powered automation that improves how we manage cloud infrastructure, from policy-as-code and drift detection to LLM-assisted runbook generation. Help define the culture of this growing team as

Join Adobe Security Engineering and help build the ML and generative AI capabilities that protect Adobe, our products, and our customers. Our team sits where cybersecurity, large-scale data, and AI meet, building models for anomaly detection, threat detection, investigation, and security analytics across some of Adobe's largest datasets. As a Staff Machine Learning Engineer, you'll design, build, and scale production ML systems spanning deep learning, behavioral modeling, embeddings, and agentic AI. You'll write code, train models, run experiments, and help shape the architecture the broader team builds on. You'll also partner closely with data, platform, and security engineers across Adobe. The Challenge Architect end-to-end ML systems for high-volume security data, turning experiments into reusable capabilities. Own the ML lifecycle, from feature engineering through training, deployment, monitoring, and retraining. Build LLM and agentic AI capabilities for security investigation, including retrieval-augmented generation. Design evaluation frameworks that measure model quality and help analysts trust and validate results. Work with data and platform engineers to scale pipelines and resolve system bottlenecks together. Mentor engineers and help guide technical direction through design reviews and hands-on collaboration. What you bring Experience running production ML systems from early experimentation through sustained operation at scale. A strong background training models with PyTorch and transformers, including behavioral modeling and anomaly detection. Comfort with distributed compute like Spark, cloud platforms like AWS, and MLOps tools like MLflow. Experience building with LLMs or generative AI, along with evaluating how they behave in production. Strong Python and SQL skills, and solid software engineering habit

About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About the role AI and machine learning are reshaping how developers debug, monitor, and ship software, and Sentry is uniquely positioned to lead that shift. We sit on a novel and massive dataset of real production errors, spans, and logs from tens of thousands of engineering organizations — the kind of signal that makes ML genuinely useful, whether it's a clustering model that groups related issues, a ranking system that surfaces the right alert at the right time, or an agent that proposes a fix. We're looking for an Engineering Manager to lead and grow our Machine Learning Engineering team. This team owns the full spectrum of ML at Sentry: classical techniques like clustering, ranking, anomaly detection, and embeddings that quietly power core product surfaces today, alongside the LLM-based and agentic systems shaping where the product is headed. You'll partner closely with product, design, and engineering leaders to decide where ML belongs in our products, what kind of ML actually fits the problem, and how we translate that work into experiences millions of developers rely on every day. In this role you will Set technical direction across the team's full ML surface area — from classical models for clustering, ranking, and anomaly detection to LLM-based and agentic systems — and make sharp calls about which approach fits each problem Define how the team evaluates and monitors ML systems in production, from offline metrics to online experimentation to model and agent observability Stay hands-on enough to review code and model designs, contribute to architecture discussions, and unblock engineers on complex ML problems Define

FEQ227R209 About the Role Databricks needs to understand what technical capability looks like across several large, fast-moving populations: employees, customers, and partners. Today that happens through role-based learning pathways and product-aligned enablement, an approach that can't keep pace with how quickly the platform, technology, and roles change. AI has also collapsed the half-life of technical proficiency, and for the first time, it makes a living, self-maintaining model of capability possible. In this builder role, you'll own systems for defining, measuring, and developing technical capability across Databricks learner audiences. You'll build a capability model that ties roles, skills, content, and credentials together and the instrumentation that shows where capability actually stands. What You'll Own Skills taxonomies, capability models, and learning context Define the skills taxonomies that make up technical capability and shape how they’re organized; what skills exist, which are adjacent, which are prerequisites, how quickly they go stale, and what sources are available to learners for developing and maintaining them. Observability and measurement Stand up instrumentation and AI-informed signals that show where capability stands and where it's drifting. Develop live signals, not a quarterly or monthly health index, to show how skills are moving and evolving. Anticipate where capability demand is heading. Product releases, market shifts, and role evolution are constant; track changes closely so emerging skills surface early and content and programs stay ahead of change. AI-native tooling Build the software that maintains the skills taxonomy and capability model, including LLM-driven skill extraction and organization, agentic pipelines that keep them current, automated drift and gap detection, and APIs that expose it all. Make the model reusable enterprise context that other systems, teams, and products build on vs. a training-only asset. Impact You'll

P-160 Who We Are Our GenAI observability and quality product provides advanced monitoring and insights for GenAI systems, giving customers real-time visibility into their system's performance, along with a suite of tools to improve the quality of the GenAI systems. With features like real-time alerts, detailed logging, and anomaly detection, customers can quickly identify and fix issues that affect the quality of their GenAI systems. This is a great opportunity to use next-gen tools for continuous monitoring, testing, and feedback, while applying cutting-edge technologies like AutoML/AutoRag, LLM fine-tuning, Prompt Optimization and Automation to improve the performance and quality of GenAI systems. We are looking for a passionate and experienced Senior Full-Stack Software Engineer to join our dynamic team. This role will offer you the opportunity to contribute to both the front-end and back-end development of our product, while also allowing you to bring your strong product sense and UI/UX design expertise to life. The ideal candidate will have a solid track record in full-stack development, with the ability to design and implement intuitive and engaging user interfaces, as well as architect scalable and efficient backend systems. What You’ll Do Full-Stack Development : Design, develop, and maintain both front-end and back-end components of our web applications. UI/UX Design : Collaborate with design teams to create visually appealing, user-friendly, and accessible UI/UX. You’ll ensure the product provides a seamless experience from end to end. Backend API Development : Architect, design, and implement RESTful APIs and microservices to support the application. Collaboration & Mentorship : Work closely with cross-functional teams including product managers, designers, and other engineers. Mentor junior developers and share knowledge of best practices in both UI/UX and back-end development. Product Sense : Contribute to product strategy by offering insights

Zoox is developing fully autonomous vehicles and the supporting ecosystem required to bring the technology to market at scale. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We believe the transition to self-driving vehicles requires a combination of elegant vision and uncompromising execution. As a Staff Closures Engineer, you will own the closures architecture for the vehicle. You will be the subject matter expert for how doors, latches, hinges, actuators, seals, and glazing behave as a system, how that system interacts with the rest of the vehicle, and where the interdependencies between it and body, EE, firmware, manufacturing, and service will surface later in the program. This is a hands-on architecture role, not a detached one: alongside setting system direction, you will personally own a set of the most difficult parts in the system and take them from concept through sourcing, validation, and launch. Our closures are unlike conventional vehicles. They are fully automated, operate at a duty cycle closer to transit than to passenger cars, carry sensor and compute integration, and have no driver to compensate for them. The person in this role is comfortable being accountable for both the architecture and the specification. In this role, you will: Serve as the technical owner and subject matter expert for the complete closures system. Understand how every component interfaces within the system and with the rest of the vehicle, and anticipate interdependencies between closures and adjacent complex systems before they become integration failures. Set and defend the closures architecture across current and future vehicle programs - mechanism topology, load paths, sealing and water-management strategy, actuation and state-detection strategy, tolerance and dimensional strategy, and the hardware/firmware boundary - making the trade-offs explic

About the Role: Tubi is seeking a highly skilled and experienced QA Automation Engineer to lead quality assurance initiatives for our cutting-edge streaming and AI-driven product features. This pivotal role involves ensuring exceptional end-to-end user experiences, robust streaming playback, and the accuracy and integrity of our AI/ML features across web, mobile, and OTT platforms. We're looking for a candidate with a strong background in streaming QA and deep technical knowledge of media workflows. You'll be instrumental in collaborating with engineering, product, and data science teams to define comprehensive QA strategies that guarantee both functional excellence and data-level quality This is a hybrid role based out of our Toronto office. You must be willing to travel to our Toronto office two days/week. What You'll Do: Design and lead test strategies for streaming workflows, playback systems, and AI-powered features. Test across platforms (web, mobile, and connected TV) to ensure functional parity and playback stability. Validate streaming performance—including ABR logic, encoding pipelines, and DRM integrations—under diverse real-world conditions. Debug with precision using tools like Charles Proxy, Chrome DevTools, ADB, and Xcode. Collaborate with data and ML teams to validate AI model updates, recommendations, and personalization accuracy. Leverage AI-assisted QA tools to enhance regression coverage, UI validation, and anomaly detection. Contribute to automation and CI/CD frameworks, driving faster, more reliable releases. Oversee QA deliverables for multiple concurrent releases and ensure seamless sign-off for production launches. Monitor live environments for playback or recommendation anomalies post-release and escalate issues promptly. Continuously improve QA processes, metrics, and reporting for streaming and AI validation. Your Background: Bachelor’s degree in Computer Science, Software Engineering, or related field, or equivalent hands-on experi

About Mixpanel Mixpanel is the leading product intelligence and analytics platform, trusted by more than 29,000 companies to help understand how people use the products they build. By combining powerful analytics with AI that knows your business, Mixpanel helps teams see what’s working, diagnose what’s not, and decide what to build next. Learn more at mixpanel.com . About the Team The Proactive Insights team is a newly formed team at the center of Mixpanel's AI-first analytics vision. With a greenfield charter, we're building the intelligent layer that transforms Mixpanel from a tool you query into a partner that works for you. We answer the question every data-driven team asks: "What changed, why, and what should I do about it?" We proactively keep users informed about what matters in their data, delivering the right insights and recommendations at the right time, to the right places, both inside and outside of Mixpanel. Some examples of what we are building: AI-powered root cause analysis : intelligent agents that diagnose why metrics changed, identify contributing factors, and recommend what to do next AI automations for KPI monitoring : always-on agents that track your key metrics and deliver insights to Slack, email, or directly in Mixpanel Intelligent alerts and anomaly detection : monitoring powered by models like TimesFM that surfaces meaningful shifts in your data About the Role As a Software Engineer on Proactive Insights, you'll build the features that transform how teams stay on top of their data. You'll work across the full stack, from React frontend experiences to Python backend services to integrating with our query engine, to ship workflows that surface insights users didn't know to ask for. You'll collaborate closely with Product and Design to shape what proactive analytics looks like, and with our AI Engine team to leverage shared infrastructure for orchestration, evaluation, and observability. This is a high-impact role on a small, fast-moving tea

As a CNC Programmer at Zoox, you'll interface with a broad range of Engineers and Craftspeople to build hardware systems for our Vehicle, Compute, Sensor, and testing fleets. Core values include a positive outlook, good interpersonal skills, and a desire to affect a company genuinely trying to change the world. Familiarity with names like Hermle, DMG Mori, Siemens, Heidenhain, Lang, Haimer, and Robotic Solutions will give you a head start at Zoox. Your experience in a professional and well-equipped CNC shop and positive outlook in a team environment will serve equally well on the path to success. Our team encompasses everything from prototyping to manufacturing operations. Career growth will be at your disposal. We serve an entire company and often volunteer to step outside our disciplines to help all who ask. If this sounds like the position you're looking for, we look forward to meeting you. In this role, you will: Manufacture parts from one-off prototypes to short-run production in a variety of materials Program 5-axis CNC machines using HyperMILL Design and machining of fixtures to hold components during machining operations Setup and teardown of CNC machines Interface with people across the company to acquire the correct set of data needed to make components Assist in ordering materials and tools for the machine shop Develop and maintaining digital tooling and work holding databases Work with engineering teams to provide DFM feedback for machined components Qualifications Extensive experience as a 5-axis CNC mill machinist 10+ years of experience as a CNC programmer using state-of-the-art CAM systems with 3D simulation and collision detection capabilities Familiarity with Heidenhain and Siemens CNC controls Strong CAD skills <p

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role We’re a team of high-output generalists where ML and systems engineering converge. This is not a "run the models" role. We reason from first principles about why a perception model learns what it learns, close the gaps that cap its performance, and raise the bar on the data and evaluation loop that drives autonomy. Your work will directly impact how autonomous systems understand rare scenarios, adapt to global geographies, and scale safely. About the work You’ll solve autonomy’s hardest data challenges through applied ML and systems rigor: Diagnose why perception models underperform on the long tail, and turn that into targeted data and training priorities. Design eval metrics and regression detection that tell us whether a model is ready. Curate and clean training data for segmentation and occupancy; hunt the data problems that silently cap performance. Run controlled ML experiments and ablations; cleanly sepa

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Machine Learning is at the heart of Lyft’s products and decision-making. Machine Learning Engineers at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges, from pricing and marketplace frameworks that ensure reliability and competitiveness, to agentic AI platforms that automate analytical workflows, to behavioral detection systems that protect the integrity of our network. We operate at the intersection of applied ML and real business impact, shipping models that directly influence revenue, rider experience, and partner trust. Lyft Business builds products that help organizations move the people who matter most—employees, customers, patients, and guests—easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs. We're looking for a Machine Learning Engineer to design, build, and deploy ML systems across Lyft Business. This is a high-scope role: you won't be siloed into one problem area. Instead, you'll move across pricing algorithms, fraud and behavior detection, agentic AI systems, and emerging ML applications as the business evolves. You'll write production-quality code, own models end-to-end from prototyping through deployment, and collaborate closely with Data Scientists, Product Managers, and Software Engineers to translate complex business problems into scalable ML solutions. This role is ideal for someone who is technically versatile, energized by variety, and wants to see th