Detection And Response Jobs

About the Team The Statsig team at OpenAI builds and operates the experimentation platform that powers product development, measurement, and decision-making across the company. We partner closely with product, engineering, and infrastructure teams to ensure experiments are trustworthy, statistically rigorous, and scalable to the needs of frontier AI products. Our mission is to help teams make better decisions through reliable experimentation. We care deeply about statistical correctness, pragmatic solutions, and building systems that researchers and engineers can trust at massive scale. The team operates at the intersection of experimentation methodology, data infrastructure, causal inference, and product analytics. We are looking for experienced experimentation experts who want to shape the future of experimentation in the AI era. About the Role We are hiring a Staff-level Data Scientist to help lead the evolution of OpenAI’s core experimentation platform. This role is focused on improving the statistical rigor, reliability, and practical usability of experimentation across the company. You’ll work on some of the hardest problems in online experimentation: sample ratio mismatch detection, variance reduction, bias mitigation, metric design, triggered analysis, heterogeneous treatment effects, sequential testing, and experimentation in complex ML systems. You’ll also help translate advanced statistical concepts into pragmatic systems and product experiences that teams can actually use. This is a highly technical individual contributor role with significant influence across methodology, platform architecture, and experimentation best practices. The ideal candidate combines deep statistical expertise with strong systems intuition and hands-on experience building or operating experimentation platforms at scale. In this role, you will: Drive the statistical direction and technical strategy for OpenAI’s experimentation platform Design and improve experimentation methodolo

The Enterprise Account Executive (Financial Services) will report to the Director of Enterprise GTM and own revenue growth across a portfolio of Scale AI’s most strategic financial services customers and prospects. This role is focused on selling complex, agentic AI solutions -autonomous workflows powered by LLMs and human-in-the-loop systems - into large banks, insurers, asset managers, and fintechs. You will operate as a strategic partner to senior executives across the business, technology, and risk organizations - helping them reimagine core workflows (e.g., underwriting, fraud detection, KYC, claims, research, and operations) through AI agents. This is a highly consultative, technical enterprise sales role requiring deep domain fluency, executive presence, and the ability to navigate regulatory, security, and multi-stakeholder complexity. You will own the full customer lifecycle - from origination through close, deployment, and expansion - while acting as the quarterback across Solutions Engineering, Product, Research, and Delivery teams to land and scale high-impact AI programs. You Will: Own and expand relationships with the largest financial services institutions (banks, insurers, capital markets, fintech), focusing on high-impact, multi-year AI transformations Sell agentic AI solutions by mapping Scale’s capabilities to mission-critical workflows (e.g., underwriting, fraud, compliance, customer ops, investment research) Build trusted relationships with executive stakeholders (CIO, CTO, Chief Data/AI Officer, Heads of Risk/Operations/Lines of Business) and guide enterprise AI strategy Develop and execute multi-threaded account plans that drive net-new revenue, expansion, and long-term platform adoption Lead complex deal cycles, including business case development, ROI modeling, and mutual close plans across new business, renewals, and expansions Partner deeply with Solutions Engineering to shape and land technically credible pilots, POVs, and production depl

About Dot Collective We are a new generation consultancy based across UK and EU and founded on the premises of the engineering excellence and empowering people to make an impact. We work with all modern tech stacks and typically run agile scrum on all our projects. About you Are you passionate about data and its transformational powers? Do you like being able to make a huge difference in a limited period of time? We might be just the right place for you. Your key skills and capabilities: Strong SQL and Python skills Data modelling experience (dimensional, relational, or similar) Experience building and maintaining backend systems Familiarity with Microsoft Azure Understanding of APIs, containers, and backend development best practices Implementing cloud-native data platforms Engineering scalable and reliable pipelines Good knowledge of distributed computing with Spark Understanding of cloud architecture principles and best practices Hands-on experience in designing, deploying, and managing cloud resources Agile ways of working Experience in cloud automation and orchestration using tools such as CloudFormation or Terraform Monitoring and performance tuning of cloud-based applications and services Nice to haves: Experience with Snowflake, dbt, CubeJS Experience with Databricks, PySpark, ML workflows Experience with containerized deployments (Docker, Kubernetes, Azure Container Apps). Frontend development experience (React, Next.js, TypeScript) Infrastructure-as-code (Terraform, CloudFormation) Model Deployment & Serving – Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance Monitoring & Retraining – Set up model drift detection, performance monitoring, and automated retraining ML Pipelines & CI/

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 Our team is growing and we are looking for experienced machine learning researchers and engineers to join us. In this role, you will apply your expertise in machine learning to solve an array of challenges in Nuro's autonomy stack — detection with sensor fusion, tracking, fine grained classification, human intent understanding, to name a few. About the Work You will be involved in all stages of problem solving, including initial proof of concept, model iteration, onboard deployment, and on-road performance monitoring and troubleshooting. You will develop techniques and/or processes that allow us to train models that are able to utilize massive scale of data, but still keep the model onboard latency in check so that it is deployable. You will have the opportunity to take a novel problem, assume the ownership, and just go deep on it. About You Recent experience in applying state-of-the-art deep learning techniques to solving auton

About Dot Collective We are a new generation consultancy based across UK and EU and founded on the premises of the engineering excellence and empowering people to make an impact. We work with all modern tech stacks and typically run agile scrum on all our projects. About you Are you passionate about data and its transformational powers? Do you like being able to make a huge difference in a limited period of time? We might be just the right place for you. Your key skills and capabilities: · Implementing cloud-native data platforms · Engineering scalable and reliable pipelines · Good knowledge of distributed computing with Spark · Understanding of cloud architecture principles and best practices · Hands-on experience in designing, deploying, and managing cloud resources · Excellent python and SQL skills · Agile ways of working · Experience in cloud automation and orchestration using tools such as CloudFormation or Terraform · Monitoring and performance tuning of cloud-based applications and services Nice to haves: (MLOps): Model Deployment & Serving – Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance. Monitoring & Retraining – Set up model drift detection, performance monitoring, and automated retraining ML Pipelines & CI/CD – Automate end-to-end ML workflows We expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform. Our promise to you We will always see you as a human being and will do our very best to support your needs and wellbeing – well-designed co-working and collaboration spaces, remote working patterns that work for you, parenting leave, sabbaticals

About Dot Collective We are a new generation consultancy based across UK and EU and founded on the premises of the engineering excellence and empowering people to make an impact. We work with all modern tech stacks and typically run agile scrum on all our projects. About you Are you passionate about data and its transformational powers? Do you like being able to make a huge difference in a limited period of time? We might be just the right place for you. Your key skills and capabilities: · Implementing cloud-native data platforms · Engineering scalable and reliable pipelines · Good knowledge of distributed computing with Spark · Understanding of cloud architecture principles and best practices · Hands-on experience in designing, deploying, and managing cloud resources · Excellent python and SQL skills · Agile ways of working · Experience in cloud automation and orchestration using tools such as CloudFormation or Terraform · Monitoring and performance tuning of cloud-based applications and services Nice to haves: (MLOps): Model Deployment & Serving – Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance. Monitoring & Retraining – Set up model drift detection, performance monitoring, and automated retraining ML Pipelines & CI/CD – Automate end-to-end ML workflows We expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform. Our promise to you We will always see you as a human being and will do our very best to support your needs and wellbeing – well-designed co-working and collaboration spaces, remote working patterns that work for you, parenting leave, sabbatical

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. We’re looking for an intelligent, driven professional to join our Bank Model Risk Management (MRM) team. This team seeks to establish, maintain and oversee an effective MRM framework to identify, quantify, monitor, mitigate and report on model risk. You will have an outstanding opportunity to work cross-functionally to develop a profound understanding of models that drive critical business decisions, and add value to the Bank by mitigating risks due to ineffective model design or model misuse. What You'll Do Full-Stack Model Validation: Conduct rigorous, independent validations of sophisticated credit/fraud models—including machine learning and traditional statistical models—focusing on conceptual soundness, data integrity, and performance stability. Advanced Quantitative Monitoring: Develop automated, independent monitoring suites in Python to track KRI/KPI drift, population stability (PSI), and feature importance shifts in real-time. Remediation & Technical Advisory: Partner with 1st-line Model Developers to drive the remediation of validation findings, ensuring models and strategies are not only compliant but mathematically robust. Audit & Regulatory Liaison: Partner with Internal Audit, Internal Controls, and Compliance to facilitate the timely resolution of audit and regulatory requests. Affirm Bank: Work for the internal Bank team to support the build out of the Bank Model Risk Management function. Support the model validation requirements for Bank owned models. What We Look For 7+ years of professional experience in a highly technical capacity, such as Credit/Fraud/Financial Risk Modeling, Model Validation, or Quantitative Analytics Deep understanding of the consumer credit lifecycle and/or fraud detection. Technical familiarity with loss forecasting/fraud prediction, a

Role Overview You are a senior product leader who wants to shape how AI transforms IT and cyber risk management at scale. In this role, you will own one of the most strategic product areas in a global GRC SaaS platform, defining how organisations identify, assess, and act on cyber risk — and how CISOs and boards get the clarity they need when it matters most. You will set and drive the product vision, roadmap, and outcomes for IT & Cyber Risk on a multi-solution platform used by enterprises worldwide. You will partner closely with engineering, design, data science, and senior leaders to build AI-native capabilities that automate risk detection, prioritisation, and reporting. Your work will have high executive visibility, real strategic weight, and direct impact on how customers manage governance, risk, and compliance. Here’s a breakdown of what you’ll do (not all of it, just the important stuff) Lead the end-to-end product strategy and roadmap for IT & Cyber Risk, aligning to company objectives and broader platform direction. Design and deliver AI-powered features (LLMs, agents, ML models) that automate risk identification, assessment, and reporting for enterprise security and risk teams. Partner with engineering, data science, and design to define technical approaches, ship high-quality releases across web, mobile, and API, and iterate using product analytics. Develop deep market and customer insight by engaging regularly with CISOs, security leaders, and risk managers, and turn those insights into clear product bets. Define, track, and communicate product KPIs, AI performance metrics, and north star outcomes to senior stakeholders, including business cases for major investments. Work cross-functionally with Sales, Customer Success, Professional Services, and Marketing to ensure successful launches and adoption of new IT & Cyber Risk capabilities. These are the essentials you’ll need to get an interview 7+ years of product management experience, includi

About the Team The Platform Systems team at OpenAI operates at the intersection of cutting-edge AI and large-scale distributed systems. We build the engineering and research infrastructure required to train OpenAI’s flagship models on some of the world’s largest, custom-built supercomputers. Our team develops core model training software and works deep in the stack - spanning collective communication, compute efficiency, parallelism strategies, fault tolerance, failure detection, and observability. The systems we build are foundational to OpenAI’s research velocity, enabling reliable, efficient training at frontier scale. We collaborate closely with researchers across the organization, continuously incorporating learnings from across OpenAI into the evolution of our training platform. About the Role As a Software Engineer, Platform Systems, you will design and build distributed systems that provide visibility into large-scale training workloads and help operate them reliably at scale. You’ll work on failure detection, tracing, and observability systems that identify slow or faulty nodes, surface performance bottlenecks, and help engineers understand and optimize massive distributed training jobs. This infrastructure is critical to operating OpenAI’s training stack and is actively evolving to support new use cases and increasingly complex workloads. This role sits at the core of our training infrastructure, blending systems engineering, performance analysis, and large-scale debugging. In This Role, You Will Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior Improve observability, reliability, and performance across OpenAI’s training platform Debug and resolve issues in complex, high-throughput distributed systems Collaborate with systems, infrastructure, and research teams to evolve platform

About the Team The Platform Systems team at OpenAI operates at the intersection of cutting-edge AI and large-scale distributed systems. We build the engineering and research infrastructure required to train OpenAI’s flagship models on some of the world’s largest, custom-built supercomputers. Our team develops core model training software and works deep in the stack - spanning collective communication, compute efficiency, parallelism strategies, fault tolerance, failure detection, and observability. The systems we build are foundational to OpenAI’s research velocity, enabling reliable, efficient training at frontier scale. We collaborate closely with researchers across the organization, continuously incorporating learnings from across OpenAI into the evolution of our training platform. About the Role As a Software Engineer, Platform Systems, you will design and build distributed systems that provide visibility into large-scale training workloads and help operate them reliably at scale. You’ll work on failure detection, tracing, and observability systems that identify slow or faulty nodes, surface performance bottlenecks, and help engineers understand and optimize massive distributed training jobs. This infrastructure is critical to operating OpenAI’s training stack and is actively evolving to support new use cases and increasingly complex workloads. This role sits at the core of our training infrastructure, blending systems engineering, performance analysis, and large-scale debugging. In This Role, You Will Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior Improve observability, reliability, and performance across OpenAI’s training platform Debug and resolve issues in complex, high-throughput distributed systems Collaborate with systems, infrastructure, and research teams to evolve platform

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Department Intro The Yield Enhancement organization partners closely with Fab and Yield Engineering teams to monitor manufacturing performance, investigate defect issues, and drive yield improvement through detailed electrical and failure analysis. Position Overview The Yield Enhancement Electrical Failure Analysis Engineer performs investigative electrical failure analysis to identify, categorize, and resolve defect issues impacting yield. This role combines hands-on electrical characterization, advanced data analytics, automation, and AI-enabled engineering workflows to accelerate defect detection, root cause identification, and yield improvement. The engineer monitors manufacturing performance, prioritizes yield-related investigations, and delivers data-driven insights and clear reporting to support business decisions and continuous improvement. Responsibilities Perform electrical and failure analysis in alignment with defined area skill guidelines and summarize findings using clear, data-driven reporting and AI-assisted documentation tools. Operate and support lab equipment while adhering to safety guidelines and supporting department and company objectives. Drive root cause investigations by correlating electrical test data, FA results, process information, manufacturing history, and AI-generated insights to identify yield detractors and recommend corrective actions. Develop and maintain yield monitoring meth

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. As a QE FDC Engineer at Micron, you'll play a crucial role in transforming how the world uses information to enrich life for all. Our ambitious team in Taichung City builds world-class memory and storage solutions, fostering an inclusive and collaborative environment where your contributions will drive operational excellence and innovation. Responsibilities and Tasks: Establish and improve process conditions and technologies to ensure flawless manufacturing operations. Upgrade process capability and reduce production costs through innovative engineering solutions. Establish and modify process management projects to improve operational efficiency and quality performance. Control process parameters for semiconductor manufacturing equipment and ensure process stability. Assess, advocate for, and develop new detection methodologies to maintain industry-leading performance. Analyze process abnormalities and implement corrective actions to sustain high manufacturing standards. Leverage AI-enabled tools, advanced analytics, and automation solutions to improve fault detection, process monitoring, troubleshooting efficiency, and data-driven decision making. Collaborate with cross-functional teams to identify opportunities for AI adoption and digital transformation that enhance manufacturing productivity, quality, and operational excellence. Requirements: Bachelor's degree in Engineering or related field,

Managing twenty-five scheduler cells by hand does not scale, and we are not going to try. NVIDIA is building a config-as-code foundation for its EDA compute farm, and we need an automation engineer to own it end to end. You are joining at the point where this is still partially manual and inconsistently applied across cells. That is the interesting version of the problem — you will be migrating off partial systems and reconciling drift, not starting from a blank repository. What you'll be doing: Designing and owning the configuration schema for LSF cell deployment, so that a policy change is written once, reviewed, tested, and applied identically everywhere it belongs. Building the deployment pipeline that takes scheduler configuration from merge to production across a federated estate, including staged rollout and rollback. Eliminating configuration drift across cells, and building the tooling that keeps it eliminated. Standing up the regression suite that lets us upgrade LSF on a schedule rather than on a dare. Working alongside our LSF internals engineer to encode hard-won scheduler knowledge into templates and policy, so that expertise lives in the repository instead of in one person's head. What we need to see: BS or MS in Computer Science or equivalent experience. 6+ years in infrastructure engineering with strong config management depth — Ansible, Salt, Puppet, Chef, or comparable. Real experience with GitOps practice at scale: review workflow, environment promotion, drift detection, and safe rollback. Proficiency in Go, Python and shell, and comfort building the tooling rather than only using it. Exper

AI Teammates are Asana’s flagship AI innovation - autonomous agents embedded directly into enterprise workflows that reason, act, and proactively drive work forward. The AI Teammates Platform (AITP) team owns the execution engine and platform layer that makes these AI Teammates effective, reliable, and scalable. We build the core systems behind agentic execution: model evaluation and rollouts, proactive detection of blocked work, execution quality infrastructure, tool orchestration, and the platform APIs that power AI across all of Asana engineering. Rather than building a monolithic feature, we build a composable foundation. If a Teammate reasons, acts, or autonomously improves - that’s us. We’re looking for people who care deeply about customers and are passionate about building products that improve the way millions of teams work together. As a member of the team developing the software of a product-led company, you won’t just code — you will be an integral part of the entire product development process, from conception to post-launch reflection. Asana is helping teams become significantly more productive with our leading work management platform. You will be manifesting continuous improvements in their experience and helping us achieve our mission to enable all teams to work together effortlessly. This role is based in our San Francisco office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements. What you’ll achieve Work in a high-performance, dynamic team with a strong focus on velocity and software quality Contribute to building a complex SaaS application that is a market leader in its segment Gain exposure to Asana’s core infrastructure, particularly

About the role: This is an opportunity to join K's critical InfoSec team as a Senior Security Engineer - AppSec and operate with foresight in protecting our infrastructure, applications, cloud security, and customer trust. As a lean team, we span across multiple areas such as AppSec, CloudSec, SecOps, ITSec, and Compliance and apply it towards reading and interpreting architecture, or planning and building out net new security solutions. You will have the autonomy to define and implement cutting-edge security solutions across our entire technical ecosystem, ensuring our innovative work remains robust and compliant against evolving global threats. This role is crucial for establishing and maintaining a world-class security posture, particularly within the sensitive and highly regulated healthcare technology space. What you will do: Lead the development and implementation of robust application security protocols throughout the entire Software Development Lifecycle (SDLC). Partner with engineering teams to incorporate security into architecture, design, development, testing and deployment Perform hands-on security testing of web applications, APIs, cloud-native services and supporting infrastructure Build and improve automated security testing within CI/CI pipelines, including static analysis, dependency scanning, secrets detection, container scanning and dynamic testing Evaluate effectiveness of application security tools, improve tooling output quality and reduce unnecessary findings and developer friction Develop secure coding standards with developer-focused documentation Contribute application security expertise to vulnerability management, during security incidents/investigations and post-incident reviews Evaluate third party applications, libraries and APIs and integrations for security risk Ensure adherence to relevant healthcare regulatory and compliance requirements (e.g., HIPAA, GDPR, etc.) across all product lines and systems. What we're looking for: 4+ yea