Signal And Growth Insights Manager in United States

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role Ramp builds software that moves real money, from bill payments and corporate cards to reimbursements, accounting integrations, and travel. Our customers rely on us to keep their operations running: invoices get paid, books get closed, and finance leaders can trust the data behind every decision. In this role, you will be the person who spots where customers are getting stuck and drives issues to a clear resolution. You will learn a complex product deeply enough to troubleshoot across payments, bank linking, accounting syncs, and card programs, then partner with teams across Engineering, Risk, Product, and Partnerships to get problems solved. This is not script-based support. You will triage, investigate, and make high-signal judgment calls throughout the day. You will thrive in this role if you are a systems thinker and deeply customer obsessed. Support is the baseline. How you think about improving processes, tools, and outcomes is what sets you apart. What You’ll Do Be the voice of Ramp's Customer Support. You'll spend your day enga

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Our Fraud Intelligence team's mission is to turn fraud signals into insight that our EPD teams transform into improvements across Protect, IDV, Signal, and Guaranteed Payments. We believe transaction patterns, device signals, identity linkages, and behavioral data are dramatically underleveraged tools in fraud prevention, and we ground our products in what adversaries are actually doing right now. As the Fraud Intelligence Lead, you will build and run a small, high-leverage team of Fraud Intelligence Analysts (and eventually a Staff Researcher) responsible for live casework across Protect, IDV, and Payments/ACH. You'll operate as a player-coach, hiring and coaching your team while staying close enough to the work to personally pick up casework and SEV response when needed. Responsibilities Team Building & People Leadership Set the casework quality bar: define what rigorous investigation, triage, and reporting look like for the team Coach analysts on investigation technique, pattern synthesis, and translating findings into product/model input Operating Model & Cross-PA Partnership Own coverage allocation across the Protect/IDV and Payments/ACH pods, including flexing assignments as volume shi

What you’ll do Partner with medical image reconstruction scientists / engineers to build ML components that improve reconstruction quality, speed, robustness, or quantitative accuracy. Define training/evaluation pipelines, datasets, and metrics that map to user needs and design requirements. Productionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks. Collaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation. Help build tooling for rapid experimentation as well as rigorous verification of algorithm changes. What we’re looking for Strong applied ML experience plus comfort with signal processing / imaging or adjacent domains. Ability to move fluidly between research prototypes and production-quality systems. Strong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility. A demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.) Useful experience ML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints. Pragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts. A background in computational physics or scientific computing. Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging. Experience in Agentic-SciML is a plus. Hands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines. Background in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned variants).

What you’ll do Be the generalist EE for the scanner system: integration, bring-up, debugging, and making the electrical side of the device reliable and serviceable. Own ultrasound experimentations that feeds the image reconstruction team Design and execute experiment setups for transducer characterization (element sensitivity, bandwidth, cross-talk mapping, beam profile measurements) and ex vivo / phantom clinical testing. Acquire, process, and analyze RF and baseband signals for data quality assessment and benchmarking. Design simple boards and adapters as needed (monitoring, power/safety, interface/conditioning), and take them from prototype through a stable revision. Prototype quickly, then harden what works: wiring/harnessing, grounding, safety interlocks, and reliable integration across subsystems. Own practical test setups and documentation (fixtures, scripts, procedures) that make experiments repeatable and results comparable over time. What we’re looking for Strong hands-on EE background with experience building, debugging, and iterating on real systems in the lab. Solid understanding of signal processing fundamentals — knows what to measure, how to condition and digitize it, and how to evaluate signal quality in the context of an imaging system (SNR, bandwidth, dynamic range, artifacts). Comfortable spanning system integration + occasional design work (schematics/layout reviews or light PCB design) in a fast-moving environment. Ability to work at the boundary between hardware and algorithms: measure reality, communicate constraints, and help close gaps vs simulation. High agency and practicality: able to set up experiments, get trustworthy data, and unblock others on a lean team. Useful experience Analog/mixed-signal, or high-speed data capture experience; strong instincts for instrumentation and noise/debugging. Ultrasound or acoustic sensor handling: hydrophone calibration and field mapping, transducer impedance characterization, element-level sensitivity

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Description of the team The Dashboard Foundations team is the product platform team that stewards the Plaid Dashboard ( dashboard.plaid.com ). As both the portal for applying for production access and also the host for many products, the Dashboard is a critical touchpoint for our customers. Our mission is to build the platform of every product engineer’s dreams, with rich tooling, abstractions, and resources available to support every phase of the software development lifecycle so that developing a high quality, secure product is fast and easy. Our customers are over a dozen teams building in the Dashboard, representing products in areas such as Fraud, Credit, Signal, Account Verification, Transfer products, and more. We are a full stack team made up of former product engineers, drawing upon our experience to set our north star vision. We have in-person members in New York and San Francisco as well as some members distributed in various locations across the United States. Responsibilities You will lead a team of 8 engineers, ranging from Junior to Staff, developing them through clear goal setting, coaching, and feedback. You’ll define and drive the long-term strategy for this foundational area, in c

About the Team OpenAI’s mission is to ensure that general-purpose artificial intelligence benefits all of humanity. The Engineering Acceleration team builds products that multiply the effectiveness of OpenAI’s technical teams, helping engineers, researchers, and product teams understand complex systems, learn from what they ship, and operate reliably at scale. As AI changes how software is built, we have an opportunity to rethink engineering workflows from first principles. We’re creating tools and shared systems that turn complex data, experimentation, and technical workflows into clear decisions and useful action. About the Role In this role, you’ll lead design across two connected product areas: a real-time data exploration and observability experience for investigating large-scale system and product behavior, and an experimentation platform for safely launching changes, measuring their impact, and deciding whether to ramp, iterate, or roll back. This is more than a dashboard-design role. You’ll define the interaction models that take someone from a vague question or unexpected signal to a trustworthy answer and clear next step. You’ll work closely with engineers, researchers, data scientists, and product teams to understand the mechanics of their work and make dense technical systems coherent without flattening the details that matter. You’ll also help establish greater consistency across OpenAI’s enterprise and internal tools, developing durable patterns that support AI-native workflows and enable other designers to build more effectively. This role is based in our Seattle, WA or San Francisco, CA offices. We offer relocation assistance to new employees. In this role, you will: Lead end-to-end design for data-intensive products used by engineers, researchers, and product teams. Shape the complete learning loop: instrument, launch, observe, investigate, evaluate, decide, and iterate. Create clear, high-craft workflows for querying, filtering, comparison, drill-d

About the Team The B2B Marketing team is responsible for helping businesses understand, adopt, and get value from OpenAI’s products. B2B marketing is a major and growing priority for OpenAI as we scale our work with companies, developers, and institutions around the world. About the Role Within B2B Marketing, Demand Generation builds the integrated, full-funnel engine that connects audience insights, content, field and digital experiences, paid media, lifecycle, and sales follow-through to qualified pipeline. We partner closely with Sales, Partnerships, Product Marketing, Communications, Creative, Web, RevOps, Analytics, and regional teams to create a cohesive customer experience and scale what works. We’re looking for a Senior Lifecycle Strategist to define how prospects and customers move through personalized, signal-driven journeys across the B2B lifecycle. You’ll own lifecycle strategy, audience and journey architecture, testing priorities, and performance recommendations while partnering closely with a Lifecycle Marketing Manager on build and delivery. Initially, the role will focus on prospect nurture, database activation, and sales handoff; over time, it will help expand our lifecycle capabilities across adoption, cross-sell, upsell, retention, and re-engagement. In this role, you will: Define the B2B lifecycle strategy, journey architecture, audience framework, communication principles, and roadmap across prospect and customer stages. Design nurture and activation programs that respond to fit, persona, segment, product interest, engagement, intent, and sales status rather than relying on one generic journey. Partner with RevOps, Data, Web, SDR, Sales, and Product teams to establish reliable triggers, scoring inputs, routing logic, suppression rules, exits, and service levels. Work closely with the Lifecycle Marketing Manager to translate strategy into clear program requirements, content needs, build plans, QA standards, and launch sequencing. Own the lifecyc

About the Team OpenAI’s Forward Deployed Engineering (FDE) team turns research breakthroughs into production-grade systems. We embed deeply with customers to solve high-leverage problems and act as the delivery engine for our most complex large-scale engagements. We move quickly from prototype to production and surface reusable patterns that shape our platform. We operate at the intersection of deployment and development – working closely with OpenAI Research, Product and Partnerships. About the Role As a Technical Deployment Lead (TDL), you will define how OpenAI delivers complex systems to Semiconductor customers. You will own how solutions are scoped, built, shipped, and adopted across high-value engineering workflows such as RTL design, verification, and physical implementation. You’ll translate business outcomes into a technical plan, run day-to-day execution across FDEs, Researchers, and Customer Engineers, and partner with customer teams to ensure delivery supports their goals. You will focus on the semiconductor vertical to deploy next-generation AI capabilities. You will own delivery end-to-end: embedding with customers to map workflows and success criteria, ensuring components ship on time, and leading readiness and change management for adoption. You’ll track progress, manage dependencies, make sequencing decisions, and drive 0→1 prototypes through MVP and scale. You will also share field insights with Product and Research to guide roadmap and priorities. Success will be measured first and foremost by impact - deployments that deliver measurable value against customer goals, drive adoption, and become critical to their workflows. Additional measures of success include delivery reliability (milestones hit, low reopen/churn), operating leverage (patterns reused across deployments), judgment under pressure, and product impact (field signal that shifts roadmaps/architectures). This is a high-trust, high-autonomy role. Success requires deep technical project m

About the team OpenAI’s Forward Deployed Engineering (FDE) team turns research breakthroughs into production-grade systems. We embed deeply with customers to solve high-leverage problems and act as the delivery engine for our most complex large-scale engagements. We move quickly from prototype to production and surface reusable patterns that shape our platform. We operate at the intersection of deployment and development – working closely with OpenAI Research, Product and Partnerships. About the Role As a Technical Deployment Lead (TDL), you will define how OpenAI delivers complex systems to customers. You will own how they are built, shipped, and adopted. You’ll translate business outcomes into a technical plan, run day-to-day execution across FDEs, Researchers, and Customer Engineers, and partner with customer teams to ensure delivery supports their goals. You will own delivery end-to-end: embedding with customers to map workflows and success criteria, ensuring components ship on time, and leading readiness and change management for adoption. You’ll track progress, manage dependencies, make sequencing decisions, and drive 0→1 prototypes through MVP and scale. You will also share field insights with Product and Research to guide roadmap and priorities. Success will be measured first and foremost by impact - deployments that deliver measurable value against customer goals, drive adoption, and become critical to their workflows. Additional measures of success include delivery reliability (milestones hit, low reopen/churn), operating leverage (patterns reused across deployments), judgment under pressure, and product impact (field signal that shifts roadmaps/architectures). This is a high-trust, high-autonomy role. Success requires deep technical project management expertise, extreme ownership of outcomes, and an ability to immerse in customer workflows and partner with customer teams to solve complex engineering problems at pace. This role is based in San Francisco. W

About the team OpenAI’s Forward Deployed Engineering (FDE) team turns research breakthroughs into production-grade systems. We embed deeply with customers to solve high-leverage problems and act as the delivery engine for our most complex large-scale engagements. We move quickly from prototype to production and surface reusable patterns that shape our platform. We operate at the intersection of deployment and development – working closely with OpenAI Research, Product and Partnerships. About the Role As a Technical Deployment Lead (TDL), you will define how OpenAI delivers complex systems to customers. You will own how they are built, shipped, and adopted. You’ll translate business outcomes into a technical plan, run day-to-day execution across FDEs, Researchers, and Customer Engineers, and partner with customer teams to ensure delivery supports their goals. You will own delivery end-to-end: embedding with customers to map workflows and success criteria, ensuring components ship on time, and leading readiness and change management for adoption. You’ll track progress, manage dependencies, make sequencing decisions, and drive 0→1 prototypes through MVP and scale. You will also share field insights with Product and Research to guide roadmap and priorities. Success will be measured first and foremost by impact - deployments that deliver measurable value against customer goals, drive adoption, and become critical to their workflows. Additional measures of success include delivery reliability (milestones hit, low reopen/churn), operating leverage (patterns reused across deployments), judgment under pressure, and product impact (field signal that shifts roadmaps/architectures). This is a high-trust, high-autonomy role. Success requires deep technical project management expertise, extreme ownership of outcomes, and an ability to immerse in customer workflows and partner with customer teams to solve complex engineering problems at pace. This role is based in NYC. We use a hy

Audiovisual Design & Integration Specialist (Experienced or Senior) Company: The Boeing Company Boeing Defense, Space & Security (BDS) / Intelligence Systems (IS) is seeking motivated and self-starting Audiovisual Design & Integration Specialist (Experienced or Senior) for a position based in Herndon, VA . Our team in the Washington, DC Metro region designs, configures, and operates advanced AV systems for U.S. Government customers. Position Responsibilities: Survey, plan, develop, design, configure, and test multi-classification AV solutions for government customers. Generate custom audiovisual system designs, including DSP layouts and systems configuration. Lead or attend customer stakeholder meetings to collect and document or validate system requirements. Research, identify, and recommend technology options to solve customer problems Draft system engineering documentation, including signal flows, line diagrams, bills of materials, rack elevations, and cable plants Support onsite installation of AV systems and lead system configuration phases Coordinate with customer facilities teams and other onsite sub-contractors and vendors during systems installation as required Support the development of end-user training materials and operations & maintenance plans Perform in-house testing and on-site commissioning at client locations Develop expertise in the industry through continuing training, certifications, and review of trade publications Basic Qualifications (Required Skills/Experience): This position requires an active Top Secret/SCI U.S. Security Clearance (U.S. Citizenship Re

Lead Electrophysics Engineer Company: The Boeing Company Boeing Defense Space & Security (BDS) is seeking highly motivated Lead Electrophysics Engineer (Level 5) to support various programs located in Huntington Beach, CA, Seal Beach, CA , or El Segundo, CA . As a part of one or more Systems Engineering Integration & Test (SEIT) teams, you will provide technical engineering and program support, while serving as the core point of contact between your payload products and the broader system team. In this role you will serve as the technical owner for a range of RF related payload products, with an emphasis on system performance, integration, supplier technical management, and cross-organizational interface management. Your products will include both experimental and operational systems, extending through all lifecycle phases (early feasibility studies through operation and disposal). An ideal candidate will be highly motivated, with a strong technical background in RF and Radar systems, space environments, and RF payload integration onto air or space platforms. Position Responsibilities: Provide expertise over a broad range of radar related activities, including performance and trade studies of radar systems and architectures Oversee development and implementation of radar signal image processing techniques Perform system engineering and integration of Radar Warning Receivers (RWR) into various systems Developing Concept of Operations for the design and utilization of RF products Perform Mission Design and payload effectiveness analysis Lead technical teams, including subcontractors, to oversee hardware and processing

About the team The OpenAI for Government team is a dynamic, mission-driven group leveraging frontier AI to transform how governments achieve their missions. Our team works to empower public servants with secure, compliant AI tools (e.g., ChatGPT Enterprise, ChatGPT Gov) and mission-aligned deployments that meet government technical requirements with strong reliability and safety. About the role Forward Deployed Engineers (FDEs) lead complex deployments of frontier models in production. You will embed with our most strategic government and public sector customers—where model performance matters, delivery is urgent, and ambiguity is the default. You’ll map their problems, structure delivery, and ship fast. This includes scoping, sequencing, and building full-stack solutions that create measurable value, while driving clarity across internal and external teams. You will work directly with defense, intelligence, and federal stakeholders as their technical thought partner, guiding adoption, maximizing mission impact, and ensuring successful deployments at scale. Along the way, you’ll identify reusable patterns, codify best practices, and share field signal that influences OpenAI’s roadmap. This role is based in Washington DC, Seattle or San Francisco. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required, including on-site work with customers. In this role you will Own technical delivery across multiple government deployments, from first prototype to stable production. Deeply embed with public sector customers to design and build novel applications powered by OpenAI models. Enable successful deployments across customer environments by delivering observable systems spanning infrastructure through applications. Prototype and build full-stack systems using Python, JavaScript, or comparable stacks that deliver real mission impact. Proactively guide customers on maximizing business and operational value from

About the Team The Hardware Health and Observability team owns the end-to-end health lifecycle of OpenAI’s global compute fleet. Our mission is to maximize healthy, usable compute across accelerator vendors, generations, cloud providers, and regions through reliable health signals, automated remediation, and scalable operational tooling. We build the systems that observe, detect, remediate, and verify hardware issues across GPUs, CPUs, networking, and platform infrastructure, enabling frontier model training and inference workloads to run reliably at hyperscale. We are the last line of defense for the success of OAI’s production and research workloads. About the Role On the Hardware Health and Observability team, you’ll build critical infrastructure that keeps OpenAI’s largest compute clusters healthy and operational at scale. Even small numbers of unhealthy systems can impact large-scale training and inference workloads. This team focuses on minimizing downtime, improving fleet efficiency, and ensuring compute resources remain continuously available to researchers and product teams. Engineers on this team own problems end-to-end, from defining health signals and debugging failures to building automated remediation systems that operate across millions of GPUs globally. In this role, you will: Define and maintain health signals across GPUs, CPUs, networking, and platform infrastructure. Build and evolve health checks that detect, remediate, and verify failures at scale. Ensure critical health checks execute with minimal latency to maximize workload uptime. Investigate hardware failures and system-level issues across large-scale compute environments. Own node lifecycle workflows including drain, quarantine, repair, RMA, and return-to-service processes. Build automation and tooling that enables global cluster management with minimal manual intervention. Partner with workload, reliability, and provider teams to integrate health signals into training and inference system

About the team The Intelligence and Investigations team seeks to rapidly identify and mitigate abuse and strategic risks to ensure a safe online ecosystem in close collaboration with our internal and external partners. Our efforts contribute to OpenAI's overarching goal of developing AI that benefits humanity. This role focuses specifically on AI Safety: understanding and mitigating risks created or amplified by increasingly capable AI systems. It is not a cybersecurity, information security, or corporate security role. The Strategic Intelligence & Analysis (SIA) team provides safety intelligence for OpenAI’s products by monitoring, analyzing, and forecasting real-world abuse, geopolitical risks, and strategic threats. Our work informs AI safety mitigations, product decisions, and partnerships, ensuring OpenAI’s tools are deployed responsibly across critical sectors. About the role We are looking for a Frontier AI Risks Lead to help us understand potential harms and misuse of AI in a time of rapid, sustained change. We seek to understand how developments in AI could intersect with misuse and abuse, accelerating existing harm areas and creating novel risks. We seek to scan available signals and use strategic foresight methodologies to enable proactive detection and mitigation of frontier AI risks. This is an AI safety role focused on frontier and systemic risks, including model misalignment, recursive self-improvement (RSI), multi-agent interaction, loss of control, runaway agents, and related emerging failure modes. In this role, you will help provide a strategic-level perspective on a range of frontier AI safety areas, producing actionable understanding of issues relevant to OpenAI’s platforms, systems, and broader mission. Utilizing mixed quantitative and qualitative methodologies, you will spot early warning signs, pull threads on potentially concerning behavior, and turn weak signals into clear, prioritized risk calls. You will focus on upstream ecosystem sc