Signal Integrity Hardware Engineer Jobs

NVIDIA has transformed computer graphics, PC gaming, and accelerated computing for more than 25 years through exceptional technology and the people who build it. In semiconductor manufacturing, our role is to enable the ecosystem, not compete within it. We partner with fabs, equipment manufacturers, and software providers to make inspection, metrology, and manufacturing intelligence dramatically faster on the NVIDIA platform. Our team builds the software that makes this possible: models, adaptation and evaluation workflows, and deployable inference capabilities that partners integrate into their own tools. We work in environments where labeled data is limited and proprietary, distributions shift across tools and fabs, production budgets are tight, and software must operate inside air-gapped facilities. We’re seeking a Principal Systems Software Engineer for Semiconductor Inspection in Santa Clara. This is a hands-on architect role: you will define the approach, build it, evaluate it, and demonstrate the results. You will work across computer vision, time-series modeling, multimodal AI, anomaly detection, model adaptation, evaluation, and production inference. Success means technology that a fab or equipment vendor can integrate, operate, and trust—not only a successful internal demonstration. What you’ll be doing: Define and prototype AI system architectures spanning optical and e-beam inspection, wafer and mask inspection, metrology, defect review, equipment signals, and process data. Advance world foundation model capabilities for semiconductor manufacturing, including vision, time-series and multimodal representation learning, model adaptation, domain transfer, and data-scarce defect understanding. Develop workflows for defect detection, classification, localization, segmentation, nuisance filtering, ADC, AD

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. Role Overview This role serves as a critical connection point between Micron’s customers, Sales organization, Business Units, Revenue Operations, and planning partners. It is responsible for translating customer demand, market intelligence, supply signals, and business priorities into actionable forecasting and planning insights that help Micron align strategic agreements, supply, optimize revenue outcomes, and proactively manage risk. This position should be understood as both analytical and highly collaborative. Success requires the ability to interpret complex data, understand customer and business context, influence cross-functional alignment, and communicate clear recommendations that support enterprise planning decisions. The role is especially important as part of Micron’s Go To Market Transformation, where stronger demand signals, improved process governance, and more data-driven decision-making are enabling a more connected and responsive planning model. Key Responsibilities Analyze and integrate key demand, supply, and market signals including Field Sales Forecast (FSF), Strategic Customer Agreements (SCA), Business Plan, Supply, Response to Forecast (RTF), the outer quarter planning process, Constrained FSF, Total Available Market (TAM), and analyst/industry insights to identify risks, opportunities, and misalignments. Proactively drive resolution of forecast, supply, and planning disconnects through cross-functional collaboration with Sales, Revenue Operations, and Business Unit

Who we are About the team Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale — building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants. Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers. What you'll do We're looking for machine learning engineers who want to build intelligent systems that provide financial data at scale. You'll play a key role in designing, training, and deploying ML models that improve the quality, accuracy, and usefulness of financial data across Stripe's ecosystem. Responsibilities Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy Develop pipelines and automated processes to train and evaluate models in offline and online environments Integrate ML models into production systems and ensure their scalability and reliability Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can im

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. Senior Process Analysis Engineer As a Senior Process Analysis Engineer in Micron’s Automation Development group at Taiwan Taichung, you will play a critical role within the Process and Process Integration team to enhance inline defense line effectiveness, improve process control capability, and prevent yield-related excursions through data-driven and AI-enabled solutions. You will take ownership of issue investigation, root cause analysis, Design of Experiments (DOE), and Continuous Improvement Programs (CIP) for critical materials and manufacturing processes. In addition, you will collaborate with cross-functional teams to integrate advanced process control methodologies, AI-assisted analytics, and digital solutions into process flows supporting next-generation high-density memory products. Job Responsibilities Leverage AI-assisted and AI-enabled technologies to improve manufacturing intelligence and decision-making, including applications in Statistical Process Control (SPC), Fault Detection and Classification (FDC), Automated Defect Classification (ADC), Internet of Things (IoT), and Data Analytics (DA). Apply AI-supported analytical approaches to identify process variations, improve inline defense line effectiveness, and accelerate root cause investigation and yield learning. Collaborate with internal and external partners, including Process Engineering, Failure Analysis, Quality, and Manufacturing teams, to correlate Defense Line (DL) signa

Must be based in Vancouver The role We're hiring a dedicated data engineer to own the production data platform that our delivery, product, and engineering teams run on; designing integrated, governed data pipelines and delivering automated reporting, AI-assisted workflows, and predictive signals on top of them. You'll write production code, design systems, own CI/CD, and be accountable for the correctness of data that leaders make decisions on. What you'll do Design and operate our cloud data platform: ingestion, transformation, orchestration and serving. Integrate data from across the business (delivery tooling, CRM, product telemetry, finance, support and customer feedback systems) with shared identifiers, data contracts and lineage. Build automated and continuously refreshed reporting so teams manage by exception rather than chasing status. Connect approved AI agents to governed data with structured outputs, provenance, guardrails and human approval in the loop. Build feature pipelines and the MLOps controls behind predictive use cases: tests, versioning, promotion gates and drift monitoring. Own the engineering standards for data: testing, observability, environment promotion, PII classification and access control. What you'll bring Strong software engineering fundamentals: production-quality code, API and interface design, testing discipline, systems design. Real experience building and operating production data platforms on a cloud warehouse or lakehouse (Snowflake and AWS preferred) with dbt and a modern orchestrator. Practical AI tooling experience: something shipped, not prototyped. LLM-backed classification, extraction or structured-output pipelines; agent and tool-calling workflows; retrieval; evals. You can reaso

Why Harvey At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. Role Overview As a Product Operations Manager, Data at Harvey, you'll be the connective tissue between our data product surfaces, Engineering, GTM, and the external technology partners we rely on — making sure customer and partner signals turn into fast, well-coordinated action. You'll own intake and triage for feature requests and data issues, run early-access programs and cross-functional launches, and manage the partner relationships that keep our data pipeline reliable for law firms and in-house legal teams pushing the frontier of AI adoption. This is a role for someone who's comfortable being in the weeds of a technical issue one hour and briefing leadership on launch risk the next — and who'd rather build a lightweight system that scales than a heavy process that doesn't get u

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. On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve. What you’ll do - You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows - You will collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field. - Strong Python skills and experience writing production-quality code. - Experience building and evaluating models for classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar). - Experience with a deep learning fra

About the role and Team At Amplitude, millions of developers instrument their products to understand how users behave. The first code they write with Amplitude is almost always through an SDK. That means our SDKs are not just integrations, they are the developer experience of Amplitude itself. We're building a new kind of SDK team: Most companies hire SDK engineers per language. We don't. We're recruiting AI-assisted SDK wizards , engineers who: learn new languages quickly use AI to accelerate development build tooling to scale SDK maintenance own developer experience end-to-end This role has unusually large scope and autonomy What You'll Work On Build and maintain SDKs across many languages (e.g. TypeScript/JavaScript, Python, Go, Java, Swift, Rust) Use AI to scale SDK development - build systems that allow a small team to maintain many SDKs Own developer experience - improve how developers integrate with Amplitude Optimize SDK for latency, performance, bundle size, resource load Create external-facing agents that take advantage of the signals collected by our SDKs Build integrations with third parties Drive high engineering standards while knowing when speed beats perfection. Create a culture where people love the work, move fast, and deliver real outcomes. You Might Be a Great Fit If: Experience building SDKs, APIs, or developer platforms. Bachelors Degree in Computer Science or relevant field. 5 years experience as a software engineer Open source library maintainership Experience across multiple language ecosystems Interest in developer experience and tooling Experience building platform tooling or code generation systems Why This Team, Why Now? Huge scope - own SDKs used by thousands of developers Polyglot engineering - work across many ecosystems AI-augmented engineering - help define the future of SDK development Direct product impact - great SDKs drive adoption of Amplitude Our values: At Amplitude, our values guide how we show up for one another and for our

Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey – from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk for one single system that is designed to meet the needs of modern day support teams. Founded in 2011, Fin became one of the fastest growing companies and remains one of the largest private software companies in the world with nearly 30,000 global businesses using our products to transform their customer support. Driven by our core values, we push boundaries, build with speed and intensity, and relentlessly deliver incredible value to our customers. What's the opportunity? As a Senior Finance Data Scientist, Existing Business , you will be the architect of the systems that predict Fin's revenue future. You will move beyond static reporting to build production-grade forecasting models that translate complex customer behaviors into financial signals. You will work on high-impact, open-ended problems, such as predicting expansion propensity and modeling long-term customer LTV. This role requires a hybrid of financial intuition and technical rigor: the ability to navigate raw data warehouses and the strategic mindset to explain the "why" behind the numbers to our leadership team. The Impact You Will Have Own and Evolve the Revenue Engine: Build and maintain predictive models for usage-based revenue, renewals, and expansion that outperform traditional linear forecasts. Unlock Predictive Insights: Develop propensity models to identify expansion opportunities and churn risks before they materialize in the ledger. Architect Finance Data: Des

Data Analyst V itD is seeking a Data Analyst V to analyze customer experience and customer support data for Reality Labs, uncover actionable insights, and improve customer satisfaction across the end-to-end customer journey. The ideal candidate will bring 8+ years of experience in data analytics or data science, strong SQL and Python or R programming skills, and expertise in statistical modeling, predictive analytics, and Generative AI to translate complex customer data into insights that drive business improvements and customer-centric decisions. Location : Remote – US (Candidates must be able to work Central or Pacific Time hours) Duration : 12 months, with potential for extension or conversion Schedule : 40 hours per week, Monday–Friday; no overtime. We provide comprehensive medical benefits, a 401k plan, paid holidays, and more. Please note that we are only considering direct W2 candidates at this time, as we are unable to offer sponsorship. Responsibilities Analyze large, complex datasets using SQL, Python or R, and statistical techniques to identify customer experience trends, pain points, and opportunities for improvement. Synthesize customer signals from surveys, digital analytics, behavioral data, operational metrics, and customer support interactions across the end-to-end customer journey. Apply statistical modeling, predictive analytics, machine learning, and natural language processing (NLP) to analyze customer support conversations, transcripts, and tickets to generate actionable insights. Develop customer segmentation and clustering models to identify common customer issues and recommend targeted solutions, including self-service resources. Integrate customer feedback with transactional, operational, and behavioral data to identify key drivers of customer satisfaction and business outcomes. Design, test, and launch customer experience and support metric scorecards to track KPIs and evaluate performance across digital, AI-powered, and human-assisted sup

Our Mission You call. You wait. You call again. In every other part of your life, you book in seconds. In healthcare, you’re blocked. We’re here to give power to the patient. For nearly 20 years, we’ve built the leading healthcare marketplace - helping tens of millions of people find and book the care they need. Now, we’re going further: building our infrastructure beyond Zocdoc’s marketplace to power access to care wherever patients search, from provider websites and insurance directories to search engines, AI platforms, and more. Healthcare still lacks something every other major consumer industry takes for granted: a seamless way to go from seeking to getting . We don’t want to own the front door to care; there isn't one. We want to make sure all of those doors open when patients are knocking. Fixing healthcare starts with fixing access to it. And we're still just getting started. Your Impact on our Mission We're building Zocdoc's provider engagement platform — a system that understands where each practice is in their journey and delivers personalized experiences that deepen their relationship with our platform. Think: intelligent recommendations, engagement scoring, AI-assisted practice management, and a provider experience that makes Zocdoc essential to daily operations. As Staff Engineer, you'll own the technical architecture for core components of this platform. You'll design systems that integrate ML models built by our Data Science team, build the data pipelines that power personalization, and ensure our product surfaces can consume intelligence seamlessly. What You'll Do Own architecture for a major domain practice intelligence APIs, recommendation serving, orchestration layer, or AI-assisted management Design data pipelines that compute engagement signals, aggregate practice profiles, and feed downstream systems Integrate ML models built by Data Science into production model serving, feature pipelines, monitoring, and

Opportunity Overview: We’re looking for a senior-level automation engineer who will help raise the bar on release quality, environment reliability, and change safety across Cohere’s platform. You’ll partner closely with Product, Engineering, Platform, and SRE to build scalable automation, guardrails, and validation systems that reduce production risk while increasing delivery velocity. This is not a “test scripts only” role. You’ll shape automation strategy, embed quality into the SDLC, and help define how changes move safely from dev → staging → UAT → prod in a fast-moving healthcare platform. You’ll help define how quality scales as Cohere grows. This role has real influence over release safety, platform reliability, and how engineering teams ship software in a regulated, high-impact domain. You won’t just test features — you’ll shape how Cohere delivers them safely to production. What you’ll do: Own and evolve Cohere’s end-to-end test automation strategy across UI, API, config changes, and critical workflows Design and maintain scalable E2E automation frameworks for multi-tenant, payer-specific workflows Build automated validation for deployment guardrails, release readiness, and production change safety Partner with Platform/DevOps to integrate automation into CI/CD pipelines and deployment workflows Create automated coverage for high-risk paths (authorization flows, partner integrations, file pipelines, feature flags, config changes) Drive test reliability, flake reduction, and actionable failure signals Define and enforce quality gates for prod releases, blue/green and canary deployments, and config changes Collaborate with Product and Engineering to ensure business outcomes are testable, measurable, and observable Improve test data management and environment stability to enable reliable automation at scale Mentor engineers on testability, automation best practices, and quality-first development Partner with SRE and Security to ensure production readines

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Role Description: We are seeking a Staff Product Manager to join our Ad Formats & Experience team within our Ads Monetization organization. This individual will work with a team that leads a portfolio of products and features within the Ad Formats & Experience team, and will drive performance optimization for our portfolio of core lower funnel formats, and our browser experience. This PM will also own core Foundational investments inc. Formats signals. You will collaborate with our Formats engineering teams, as well as our UX Design, Data Science and Product Marketing teams to shape our product roadmaps to ensure we deliver the best outcomes for our advertisers, users and Reddit’s business. Additionally, you will partner outside of the Ads Product organization with our Consumer Product teams to help think through new opportunities for collaboration and help to address how our formats evolve to our changing Consumer experiences. Responsibilities: Drive product strategy & execution for a portfolio of lower funnel formats, custom browser / post-click optimizations, and format foundations Work with other Ads Products PMs to understand dependencies, and prioritize product work across the team Work closely with User Experience Design and Research to develop new Reddit-unique ad experience, and drive them to launch Partner with product marketing and sales team to develop go-to-market strategy, integrate market feedback, and manage product launches Support on-going improvements t

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Our Partner Experience team is building an AI-native operating model. We already run a growing set of partner-facing systems: a partner portal, CRM workflows, enablement and certification platforms, and the trackers that connect them. The systems lead on our team owns that foundation — the portal, the data model, and the integrations that keep partner records accurate and flowing. This role is the other half of that build. As our AI Operations Engineer, you'll design, build, and run the agentic workflows that sit on top of those systems: Claude-powered agents and automations that triage partner applications, draft and send partner communications, sync data across tools, surface deal and enablement signals, and take routine operational work off the team entirely. Where the systems lead builds the rails, you build what runs on them. This is a builder role, not an analyst role. You'll ship working automations weekly, own them in production, and continuously expand what the team can do without adding headcount. Key responsibilities: Design and build agentic workflows (using Claude, MCP connectors, and API integrations) that automate core partner experience operations: application triage, onboarding, deal registration, certification tracking, partner communications, and reporting Partner closely with our systems lead to integrate these workflows with the partner portal, CRM, enablement stack, finance systems, and database systems — consuming and writing back to the systems of record rather than creating shadow processes Turn recurring manual work into reliable, monitored automations: define the workflow, build it, test it ag

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. Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve. What you’ll do - You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows - You will collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field. - Strong Python skills and experience writing production-quality code. - Experience building and evalua