Signal And Growth Insights Manager in United States

About the Team: OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. In this role you will: As a Hardware Test Engineer, you will work on Machine Learning/AI hardware system projects to craft the solutions for current and future data center deployments. You will bring a strong understanding of hardware system testing, excellent project management skills, and the ability to collaborate across multiple teams to ensure efficient lab operations. You will be responsible for designing, implementing, and executing comprehensive test plans that ensure the reliability, performance, and scalability of our supercomputing hardware systems. You will develop detailed test plans and methodologies tailored to hardware components, including processors, memory modules, custom accelerators and interconnects. You will collaborate with hardware design, manufacturing, firmware teams and vendors to identify, analyze, and resolve issues affecting hardware, power, thermal and high-speed interconnects. You will perform in-depth debugging on the hardware system Excellent analytical skills to diagnose hardware issues, troubleshoot problems, and propose solutions. Ability to interpret complex test data, identify trends, and draw meaningful conclusions. High-speed links, with a focus on SerDes (Serializer/Deserializer) technology to assess signal integrity, error rates, and overall link performance. You will collaborate with the lab manager to maintain the equipment and hardware systems, including oscilloscopes, thermal test chambers, liquid cooling systems, and other mea

We are seeking a detail-oriented PCB Layout Engineer to maintain electronic component libraries and develop printed circuit board layouts. This role works closely with electrical engineering, mechanical engineering, procurement, and NPI to ensure component data and PCB designs are accurate, consistent, and ready for production. This position is in Austin, Texas. Key Responsibilities Create, verify, and maintain schematic symbols, PCB footprints, and associated 3D models. Validate component pin assignments, dimensions, land patterns, and library attributes against manufacturer documentation. Maintain library naming conventions, revision control, and component approval processes. Develop PCB layouts from approved schematics, including component placement, routing, stackup coordination, and design rule setup. Collaborate with engineers to address electrical, mechanical, thermal, and manufacturing requirements. Run design rule checks, resolve layout issues, and participate in design reviews. Prepare fabrication and assembly deliverables, including Gerber or ODB++ files, drill files, drawings, and pick-and-place data. Coordinate with PCB fabricators and assembly suppliers to resolve manufacturing questions. Support engineering changes and maintain accurate release documentation. Work with procurement and engineering to identify obsolete components and evaluate suitable alternatives. Required Qualifications Experience with setting up PCB design and electronic component library management. Proficiency in Cadence Allegro/OrCAD. Ability to interpret schematics, component datasheets, and mechanical drawings. Working knowledge of PCB fabrication, assembly processes, and design for manufacturability. Familiarity with relevant IPC standards for PCB design and component land patterns. Strong attention to detail, documentation skills, and ability to manage competing priorities. Experience with multilayer, high-density, mixed-signal, and high-speed PCB designs. Familiarity w

Job Details: Job Description: Intel Corporation is the world's largest semiconductor company and a global leader in computing innovation. Our Quantum Computing Group represents Intel's bold venture into the next frontier of computing technology, leveraging our decades of semiconductor manufacturing expertise to develop silicon-based quantum processors. We're pioneering spin qubit technology in silicon quantum dots, combining cutting-edge quantum physics with Intel's unmatched manufacturing capabilities to create the world's first commercially viable quantum computers. Our collaborative approach spans Intel's research divisions and partnerships with leading academic institutions worldwide. We are seeking an exceptional Quantum Interposer Design Engineer to join our pioneering quantum computing team in Oregon. In this role you will be responsible for physical design and integration of advanced packaging interposers within a multidisciplinary team. You'll play a crucial role in advancing Intel's silicon quantum dot technology while contributing to the development of scalable quantum systems that will revolutionize computing. Key Responsibilities - Physical design and integration of interposers used within advanced packaging for quantum computing - Work closely with stakeholders to meet design requirements - e.g., receive feedback from silicon testchip, signal integrity (SI), power integrity (PI), thermal simulation, and system integration engineers - Design validation and verification; including DRC and LVS - Deliver and disseminate design milestones to stakeholders - Communicate designs in presentations and relevant documentation - Stay current with the latest developments in packaging technology and design techniques by attending conferences and reading papers As

What you’ll do Act as the in-house electrical lead for Midjourney Medical: own the electrical architecture of the scanner and the technical direction for all board-level design. Own complex board design end-to-end: architecture, schematic capture, layout (high-speed digital, analog/mixed-signal, power), DFM/DFT, fabrication and assembly vendor management, bring-up, and revision control. Write firmware for embedded targets (MCU/SoC): drivers, real-time control loops, safety-relevant logic, bootloaders, and field update paths. Audit and update HDL (FPGA) code for high-throughput data acquisition, timing/synchronization, triggering, and pre-processing of ultrasound and sensor data streams. Define electrical interfaces and data contracts with software, recon/ML and mechanical teams: timing budgets, clocking/sync, signal integrity, connectors/harnessing, and failure modes. Establish electrical engineering rigor: design reviews, schematic/layout review checklists, bring-up procedures, test fixtures, and documentation suitable for a regulated medical device program (DHF, traceability, change control). Mentor and grow the electrical function; select and manage external design partners where leverage is high. What we’re looking for Deep experience designing complex boards from blank page to stable revision, including high-speed digital and analog/mixed-signal domains. Strong schematic and layout skills (Altium/KiCad or equivalent) with real signal integrity, power integrity, grounding, and EMI/EMC instincts. Solid embedded firmware background in C/C++ (and Python for tooling): peripherals, DMA, interrupts, real-time constraints, and debugging on hardware. Practical HDL experience (VHDL/Verilog/SystemVerilog) for data acquisition, timing, and streaming interfaces. Track record of owning bring-up and debug on real hardware: scopes, logic analyzers, and disciplined root-cause analysis. Technical leadership: clear trade-offs, strong written documentation, and the ability to set

About Graphcore Graphcore is a global leader in artificial intelligence computing systems. We design advanced semiconductors and data center hardware that provide the specialized processing power needed to advance AI while improving the efficiency required for broad adoption. As part of SoftBank Group, Graphcore belongs to a family of companies developing transformative technologies. Our AI Engineering Campus in Austin plays an important role in building the hardware platforms that support the next generation of AI systems. The Opportunity As a Systems Engineering Intern, you will contribute to projects that combine hardware, firmware, and software engineering for advanced AI compute platforms. You will work with experienced engineers on subsystem design, laboratory testing, system validation, automation, and performance analysis. The internship provides hands-on experience with modern hardware development and system-level engineering. You will own clearly defined technical tasks with guidance from the team and document your methods, results, and conclusions. What You Will Do Support the design and testing of CPU and high-speed input and output subsystems for advanced compute platforms. Run laboratory tests and measurements to help evaluate performance, power, signal behavior, and reliability. Contribute to system-level validation by creating scripts and tools that streamline testing, data collection, and analysis. Explore emerging input and output technologies, including PCIe 6.0 and 800G Ethernet, and learn how they support advanced computing workloads. Assist with investigations into platform power, cooling, and energy efficiency, including liquid-cooling systems for high-performance processors. Use power meters, oscilloscopes, logic analyzers, or comparable lab equipment under appropriate supervision. Analyze test results, identify unexpected behavior, and work with engineers to reproduce and investigate issues. Collaborate across hardware, firmware, software, m

About Graphcore Graphcore is a global leader in artificial intelligence computing systems. We design advanced semiconductors and data center hardware that provide the specialized processing power needed to advance AI while improving the efficiency required for broad adoption. As part of SoftBank Group, Graphcore belongs to a family of companies developing transformative technologies. Our AI Engineering Campus in Austin plays an important role in building the hardware platforms that support the next generation of AI systems. The Opportunity We are looking for a recent graduate or early-career engineer to join the Hardware Platform Development team as a Graduate Systems Engineer. You will contribute to the design, integration, validation, and performance analysis of advanced AI compute platforms. You will work with experienced hardware, firmware, software, mechanical, thermal, and systems engineers throughout the development lifecycle. The role combines subsystem engineering, hands-on laboratory work, test automation, data analysis, troubleshooting, and clear technical documentation. Start: September, 2027 Location: Austin, Texas, USA What You Will Do Contribute to the design, integration, and testing of CPU and high-speed input and output subsystems for advanced compute platforms. Take ownership of defined engineering tasks from requirements and test planning through execution, analysis, and technical review. Develop system-level validation plans, procedures, scripts, and tools that improve test coverage, repeatability, data collection, and analysis. Evaluate platform performance, power, signal behavior, reliability, and interoperability using laboratory measurements and system data. Investigate emerging input and output technologies, including PCIe 6.0 and 800G Ethernet, and assess their use in advanced computing systems. Support platform power, cooling, and energy-efficiency investigations, including liquid-cooling systems for high-performance processor

NVIDIA's accelerated computing platforms move data at speeds that push the limits of what silicon and physics allow. Whether a high-speed interface trains reliably, maintains accurate margins, and survives every platform topology it will ever see is a question we answer ourselves. This role does that work. The Silicon Co-Design Group leads the boundary between what was designed and what was built. When a GPU, CPU, or SoC ships with interfaces that work at scale, this team is the reason. Most engineers debug within a layer. You will own the full stack. When an interface fails to train, the link margin is unexpectedly tight, or a customer reports a critical silicon issue, you trace the problem through protocol behavior, signal integrity, firmware, platform topology, and silicon marginalities. You then confirm that the fix works. Your methodology shapes how NVIDIA validates high-speed interfaces across generations. Your decisions affect yield, production ramp, and field quality. This is not a coordination role. The engineers who do it well hold protocol depth and system breadth simultaneously, never lose the thread across hardware, firmware, and software, and have the judgment to know when to go deeper and when to act. They are rare. Should that describe you, read on. What you'll be doing: Own post-silicon bring-up, characterization, validation, and debug of PCIe, NVLink, C2C, and other HSIO interfaces across NVIDIA GPUs, CPUs, and SoCs from first power-on through production readiness. Close the hardest failures. Drive root cause across protocol behavior, signal integrity, firmware and driver interactions, platform topology, and silicon marginalities and own every fix through to confirmation. Define validation strategy. Set test coverage, debug priorities, margining methodology, and stress criteria f

Work Flexibility: Onsite ​​​ Schedule 1st Shift: Mon – Fri, 8am – 5pm Overtime based on business needs What you will do As the Senior EMC & RF Test Technician, you will contribute to global regulatory compliance efforts through the execution of testing activities, development of in-house laboratory capabilities, and support of projects that ensure products meet evolving international product safety and EMC standards.​​ ​​​Perform and document electromagnetic compatibility (EMC) and radio frequency (RF) testing on new and existing medical devices to demonstrate compliance with applicable domestic and international standards. ​Operate, maintain, troubleshoot, and verify EMC and RF laboratory equipment, including shielded rooms, EMC chambers, antennas, receivers, signal generators, amplifiers, and associated test instrumentation. ​Configure, build, modify, and verify product test setups and support equipment to meet EMC and RF testing requirements. ​Support the development, expansion, and continuous improvement of in-house EMC and RF testing capabilities to meet evolving technology and compliance requirements. ​Collaborate with engineering teams to develop test strategies, execute compliance testing, and support product development activities. ​Investigate, troubleshoot, and document test failures, observations, and anomalies, communicating findings and recommendations to engineering and project teams. ​Create, update, and maintain test procedures, protocols, job-aids, work instructions, and laboratory documentation in accordance with quality system requirements. ​Evaluate, recommend, procure, and maintain laboratory equipment, software, and tools necessary to support current and future EMC and RF testing capabilities. ​Identify and implement improvements t

About the Team The Product & Platform teams at OpenAI are responsible for delivering the company’s most impactful offerings—such as ChatGPT, our API platform, and new enterprise capabilities—to a global and diverse customer base. These systems must perform at scale and deliver exceptional experiences to developers, consumers, and businesses alike. The ChatGPT Multimodal team works across voice, image generation, and other multimodal experiences to turn frontier research capabilities into reliable products. The team connects product usage and failure patterns with research, evaluation, data, inference, capacity, and external partnerships so that model and product improvements translate into better experiences for users. About the Role We are seeking a Technical Program Manager to build the flywheel that helps ChatGPT multimodal products learn from real-world usage and improve quickly. You will lead programs spanning production-signal mining, evaluation and data pipelines, research-to-production parity, multimodal capacity planning, and complex cross-functional dependencies for voice and image-generation launches. You will work closely with product engineering, research, Human Data, inference and capacity teams, safety partners, and external vendors or product partners. Success requires technical depth, strong systems thinking, comfort with ambiguity, and the ability to turn fragmented or manual work into durable mechanisms that teams adopt. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Build a system for mining production conversations and product signals to identify representative multimodal workflows, user needs, and failure modes. Establish and maintain evaluations for the highest-priority multimodal behaviors and use cases, with clear coverage, quality standards, and ownership. Package production signals into decision-ready data and

At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀 ABOUT THE ROLE We are hiring the next generation of product managers. AI has collapsed the work that used to sit between an idea and a working version of it, and for the first time, PMs can be true builders. The PMs who win in this era do not manage the work. They build the product, every day, with their own hands. A Senior Product Manager owns a meaningful surface of ClickUp and is accountable for whether it gets better. You decide what to build and why, then go build a working version of it. You prototype in Cursor and Claude when an idea is faster shown than written. You live in the data and the customer signal, and the time between spotting a problem and having something people can react to is measured in days, not weeks. The PMs who compound the most impact at this level are the ones who keep finding leverage that did not exist last quarter. KEY RESPONSIBILITIES Own the vision, roadmap, and delivery for a defined product surface, balancing near-term wins with longer-horizon investments. Run continuous discovery. Building and using agents to monitor and synthesize that part of your job. Write sharp PRDs and specs that give engineering and design what they need to move fast without ambiguity. Define success metrics before you build, track them after you ship, and iterate when outcomes do not match expectations. Prototype your own ideas using Cursor, Claude Code, and our Prototype Playground. Show the concept before filing the ticket. Collaborate cross-functionally with engineering, design, analytics, and GTM without needing to be managed through the process. Present your roadmap and results clearly

As Engineering Manager for Threat Detection, you will lead a high-performing team that powers Datadog's detection program. Threat Detection is the organization responsible for keeping Datadog ahead of an evolving threat environment: closing coverage gaps faster, raising the bar on signal quality, and shipping detections that hold up under the scale and complexity of cloud-native infrastructure. Your team will combine direct detection expertise, platform engineering, and applied AI to ship detections at a pace and scale traditional rule-writing alone cannot match. Examples of what your team will work on include detection-authoring agents, the detection platform that powers every rule in production, coverage analysis, alert triage and response automation, and the evaluation infrastructure that holds these systems to a high bar of fidelity. Detection authorship is a shared responsibility across the organization, and your team will contribute both by building the systems that scale our authoring capacity and by writing detections directly when their domain expertise is the right tool. You will partner closely with our Security Incident & Response Team (SIRT), Cyber Threat Intelligence (CTI), AI Engineering teams, and Datadog's broader Security organization. This is a high-impact leadership role: you will grow a team of security and software engineers responsible for building and executing our detection and AI strategy. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Lead the strategy, roadmap, and execution of Datadog Security's shift to AI-accelerated detection and response. Drive development of high-fidelity detections as a shared responsibility across the organization, ensuring your team's systems and direct contributions raise the bar on coverage and

About OpenAI OpenAI’s mission is to ensure that general-purpose artificial intelligence benefits all of humanity. We build models and products that help people learn, create, and solve problems—and we work to do so safely and responsibly. About the Team OpenAI’s products are talked about by people, not just press and pundits. More than 900 million people use our tools each week, learning from one other and passing along what works. Their stories shape our reputation and encourage others to try our products. Our community team builds direct relationships & channels with people who use our tech then amplifies their stories and use cases so peers can learn from them and channels their insights to our product and research teams. About the Role We are seeking an exceptional Technical Community Program Manager to help manage and scale a high-signal community of advanced ChatGPT Pro users. This is a hands-on, technically fluent role at the intersection of community-building, product enablement, user research, product education, and editorial storytelling. You do not need to be a full-time software engineer, but you should be comfortable understanding technical workflows, asking sharp technical questions, using the latest AI tools, and helping advanced users explain what they are building. You will report to the Head of Pro Subscriber Community and will be based in New York City. In this role, you will: Run and grow a high-signal community Help manage and deepen engagement with the ChatGPT Pro cohort of advanced users across disciplines Design and execute high-touch community programming, including product demos, office hours, show-and-tell sessions, peer-learning formats, and in-person events Build repeatable systems for onboarding, engagement, retention, and member communications Build deep relationships with exceptional users Identify, recruit, and onboard new individuals doing high-impact work with ChatGPT and Codex Conduct in-depth interviews and maintain ongoing r

About the team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a researcher working on Frontier Evals & Environments, you will help build north star model environments to drive progress towards safe AGI/ASI. Your work will directly guide the research programs of the most ambitious training runs happening at OpenAI. Some prior open-sourced evaluations built by researchers in this role include GDPval , SWE-bench Verified , MLE-bench , PaperBench , and SWE-Lancer . If you are interested in feeling firsthand the fast progress of our models, and steering them towards good outcomes, this is the role for you. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you'll: Create ambitious RL environments to push our models to their limits, and measure frontier

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of Agent Post-Training, Connectors, you will teach models how to interface with the top professional software using code. You will help train agents to use code, APIs, tools, and structured integrations to operate across applications like Slack, Google Workspace, GitHub, Notion, Linear, Salesforce, and other core systems of work. You will help enable models to take useful actions across a user’s digital context: finding information, updating systems, coordinating work, generating artifacts, and completing multi-step workflows through the tools teams already use. You will train models to be supercharged by the world’s most important productivity and enterprise software, turning connected tools into a powerful action surface for our agents. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people.

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of Agent Post-Training, you will improve the capabilities, reliability, and product fit of OpenAI's agentic models. You might own a research direction, build the infrastructure that makes large training runs faster and more trustworthy, create evals that reveal where models fail, or drive a capability from an idea through experimentation, integration, and launch. This role is intentionally broad. The strongest candidates are not defined by one method or subfield; they are people who can take an ambiguous capability problem and make progress across research, engineering, data, evals, and product. You should be excited to work on models that act in the world: writing and debugging code, using tools, calling functions, operating computers, collaborating with other agents, and completing valuable work on behalf of users. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, meas