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Work Flexibility: Onsite Schedule - 1st Shift Monday to Friday 8am - 4:30pm Overtime based on business needs Rotational on-call responsibilities What you will do - Responsible for accurate inventory tracking and record keeping, warehouse inventory cycle counting and optimizing inventory utilization Arrange merchandise for transport (on delivery and return) and at customer locations Read maps and route configuration Perform safety inspections in transportation setting Load, unload, or stack containers, materials, or products while checking for sterility, missing/broken implants & instruments Perform and/or schedule preventative and regular minor maintenance on company delivery vehicle, (fuel, fluid levels, tires, etc.), and keeps accurate maintenance records Advise supervisor when repairs or extensive maintenance are required for the company vehicle Field customer complaints, address and communicate as necessary to Branch team members Complete other duties as assigned What you need - Required Qualifications: Must possess a valid driver’s license with no restrictions. Must have the ability to work flexible hours, as needed to support the business needs, including weekend and evening call as needed Must have the ability to lift, push, pull and carry up to 50 lbs. Preferred qualifications: High school education or GED equivalent 1&#43; Year(s) of experience $ 22.10 per hour plus bonus eligible &#43; benefits. Travel Percentage: 10% <p style="text-align:inher

We are looking for a strategic and execution-focused Product Marketing Manager to lead go-to-market strategy for NMI's card-not-present (CNP) processing and Value Added Solutions (VAS) portfolio, the tools and services that power secure, high-converting online and card-not-present transactions for merchants, software platforms and partners across the United States. NMI has evolved from a payment gateway into a full-service payment platform. Our CNP processing stack and VAS suite, including Customer Vault, Automatic Card Updater, Invoicing, ACH and fraud prevention tools, give merchants and partners a single, trusted path from sign up to pay out. This role supports that evolution, helping translate processing and VAS capabilities into messaging that speaks to the outcomes online merchants, marketplaces, subscription businesses and software platforms actually care about: conversion, fraud loss reduction, recurring revenue retention and total cost of acceptance. You will lead market positioning, messaging, launches and enablement for NMI's CNP processing and VAS offerings, working closely with product, sales and channel teams to help make sure NMI is the preferred processing and VAS partner for every merchant and partner selling online. This role calls for a solid understanding of how ecommerce merchants, subscription businesses and software platforms evaluate and adopt payment processing and value added services, and the ability to help translate technical capability (tokenization, network tokenization, recurring billing, customer vaulting, PCI scope reduction, fraud and chargeback tools) into outcome-led messaging that supports pipeline and adoption. This role is also NMI's evangelist for CNP processing and VAS. You will present to sales teams and internal stakeholders, and help represent NMI at ecommerce and payments industry events. You will build AI tools into your everyday workflow as a core habit rather than an occasional add-on, and bring enough technical fluen

About Inspira Education Inspira Education Group is one of the fastest-growing edtech startups in the US. We started with a simple mission to democratize access to high-quality coaching so that every student in the world has an equal opportunity to access the best opportunities. As the world’s leading network of top admissions coaches in medical, legal, business, and college studies, we’re building software and services in one place—disrupting long-entrenched application processes with products and experiences that strive to provide an equal platform for candidates from diverse backgrounds worldwide. As one of the fastest-growing edtech firms in the world, we are backed by some of the leading venture capital firms and investors in the world, including Zeev Ventures, Quiet Capital, Craft Ventures and Jeff Fluhr (Founder of Stubhub). About the role We’re looking for a strong full-stack engineer who can own the complete product development process: understand a business problem, define the solution, design the user experience, build the software, and improve it after launch. You’ll work closely with leadership and business teams, combining hands-on engineering with product management and design responsibilities. You should be highly effective with AI coding tools and have the technical depth to independently review, debug, secure, and maintain everything you ship. What you’ll own Translate business needs and user feedback into product requirements, user flows, prototypes, and prioritized development plans. Design and build polished applications across the front end, back end, database, and integrations. Make architecture decisions and scope releases that balance speed, reliability, and future maintainability. Use AI tools throughout development to accelerate implementation, testing, debugging, and documentation. Own deployment, production monitoring, incident resolution, and ongoing improvements. Measure whether your work improves adoption, conversion, opera

NVIDIA DGX Cloud is an AI Factory designed to power the next generation of AI and industrial-scale breakthroughs. As a Principal Engineer for Security Architecture, within our Security Engineering organization, you will own a core security domain of the AI factory: the architecture, the paved road that delivers it, and much of the code underneath. You will hold the security design bar across DGX Cloud from inside the teams doing the building, and this is a founding seat on a new team. Security Engineering is a new organization at DGX Cloud, accountable for the security outcome of the platform, and Security Architecture is the function inside it that holds the design bar. Security here is fleet horizontal and stack vertical, so your work will cross every DGX Cloud engineering organization: you will embed with the teams building GPU clusters, control planes, and services, join their designs as a participant rather than an approver, and leave behind systems in which an entire class of risk is no longer possible. There is no architecture review board here and no approval queue. You are a senior IC with deep security domain knowledge, and the security bar holds because you helped set it and then helped ship it. What You Will Be Doing: Own a Security Domain End to End: Take architectural ownership of a core domain of DGX Cloud security, from the design through the system running in production. That could be tenant and GPU workload isolation, workload identity, infrastructure and network, supply-chain provenance, hardened baselines and patching, or deploy-time policy and admission control. Embed with the Teams Building It: Join the design early, write the code, and help land it. The posture is not &#34;you did this wrong.&#34; It is &#34;here are the considerations we need to meet, I will help, let's go to work.&#34; Build Paved Roads, Not

NVIDIA is at the forefront of the AI and robotics revolution, and NVIDIA’s robotics teams are on a mission to build the essential technology that can enable any company to become a robotics company. The Seattle Robotics Lab is uniquely positioned at the intersection of open academic research and real-world industry impact, pursuing fundamental and applied robotics research across the full robotics stack, including perception, planning, control, reinforcement learning, imitation learning, simulation, and robotics foundation models. This research aims to transform research paradigms, transfer into NVIDIA’s robotics and simulation products, and create new robotics markets for the world. The Seattle Robotics Lab has published over 500 research papers, including many influential works that have been presented at top robotics, AI, and computer vision conferences. These works include BayesSim , cuRobo , DeXtreme , DiSECT , Factory , GraspNet , IndustReal , ITPS , LAPA , <a href="h

NVIDIA DGX Cloud is an AI Factory designed to power the next generation of AI and industrial-scale breakthroughs. As the Distinguished Engineer for Security Architecture, within our Security Engineering organization, you will set the security design bar for an AI factory of hundreds of thousands of GPUs, and then build against it alongside the teams. This is the founding architecture seat in a new organization. Security Engineering is a new organization at DGX Cloud, accountable for the security outcome of the platform, and this is the architecture function inside it. You will define the security design standard for DGX Cloud, a bar that sits above the company floor, and hold it from inside the teams doing the building. Security here is fleet horizontal and stack vertical, so your scope runs from the hardware root of trust and the hardened baseline, through tenancy and GPU workload isolation, to the services and APIs built on top, across every DGX Cloud engineering organization. A small team of Principal Engineers will report to you and hold the bar at domain depth. This is still a hands-on seat, and you stay in the design with them. You will also serve as DGX Cloud's technical interface into NVIDIA's central security organization. There is no architecture review board here and no approval queue; the bar holds because the strongest security engineers in the room helped set it and helped ship it. What You Will Be Doing: Set the DGX Cloud Security Bar: Own the security design standard across DGX Cloud (tenancy, GPU workloads, identity, supply chain, and isolation) and make it concrete. Reference architectures, golden paths, and requirements engineers can actually build against, not a policy library. Hold the Bar by Building: Embed with engineering teams on real work: join the design, learn the code, help ship the thing rather than grade it afterward.

NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company”. We are looking to grow our company, and grow our teams with the smartest people in the world. What you’ll be doing: You will work with ground breaking technologies for the Tegra SoC and various NVIDIA embedded platforms Implement power and thermal management software features in Linux Kernel and user space Collaborate with power architects, hardware and software engineers on platform power estimation and optimization Optimize the software stack to improve performance, efficiency, and responsiveness for edge AI and robotics use cases. Focus on improving compute and memory utilization, reducing latency and power consumption, and tuning system-level performance to deliver reliable and scalable AI workloads across demanding real-world edge environments. What we need to see: MS in CS, CE, EE, Systems Engineering or related software/hardware engineering major, or equivalent experience 8&#43; years of software development experience with a significant focus on Linux Excellent C programming/debugging skills within Linux kernel and user space software Background with working on embedded systems and ARM processor specific System-level debugging experience and problem-solving skills Excellent communication skills Ways to stand out from the crowd: Understanding of the Linux power and thermal management features (schedule

NVIDIA is seeking outstanding Research Interns to join the Data-Driven AI for Robotics (DAIR) group. The focus is on learning embodied skills from large-scale human data. Our objective is to develop AI systems that capture, understand, and reproduce complex human motion and interaction skills across physical and digital embodiments, including humanoid robots and animated characters. Our research spans the full stack: reconstructing human motion and human-object interactions from video; generating diverse, controllable character behaviors; transferring motion across embodiments; and training physically grounded controllers for humanoid robots and interactive virtual characters. You will collaborate with a passionate and supportive research team that consistently produces influential work published at leading computer vision, machine learning, graphics, and robotics conferences. You will also have the opportunity to collaborate with world-class research and product teams across NVIDIA, following our strong “one-team” culture. What you'll be doing: Innovate and implement novel AI algorithms that transform large-scale human data into controllable motion and interaction skills across physical and digital embodiments. Develop robust, scalable training and inference pipelines for motion reconstruction, generation, retargeting, and character and robot control. Build methods that transfer human skills to humanoid robots, including whole-body loco-manipulation and dexterous manipulation. Maintain a close, collaborative relationship with your mentor(s). Publish your research findings at leading computer vision, machine learning, graphics, and robotics conferences. Partner with product teams to enable effective technology transfer of your work. Research Topics Include: Human motion and human-object interaction reconstruction, synthesis, and generatio

About the Team OpenAI Consumer Devices is building the next generation of products that bring powerful AI into people’s everyday lives. Guided by OpenAI’s mission to ensure AGI benefits all of humanity, our team combines world-class researchers, engineers, designers, and operators who care deeply about creating useful, intuitive, and responsible technology. You’ll have the opportunity to work alongside exceptional people on ambitious, zero-to-one challenges at the intersection of hardware, software, and AI. This is a chance to help define an entirely new category of products—and shape how people experience AI in the future. Our team works across custom silicon, embedded systems, operating systems, and cloud services to build reliable consumer devices and the platforms behind them. We connect kernel development with the broader software stack to deliver complete product capabilities. About the Role As an Operating Systems Engineer focused on the Linux kernel, you will design, develop, and maintain the kernel capabilities that underpin OpenAI’s consumer devices. You’ll bring deep expertise in one or more Linux kernel subsystems and carry solutions through the higher-level software stack. Your ownership will extend into the userspace services, libraries, tools, and interfaces needed to deliver complete product features. You’ll shape the boundaries between kernel and userspace, make design decisions across the stack, and see your work through development, integration, and production. In this role, you will: Build kernel capabilities: Design, implement, and maintain Linux kernel subsystem changes that support device capabilities and product requirements. Own features across the stack: Choose appropriate kernel and userspace boundaries, and build the interfaces and supporting components needed to deliver reliable features in shipped products. Debug complex system behavior: Use tracing, profiling, instrumentation, and diagnostic tools to resolve correctness, concurrency, p

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem. This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us. THE ROLE Join the Pipeline Engineering team at Everpure™ as a Full Stack Software Engineer to build and scale the pipeline visibility layer for FlashArray, FlashBlade, and Hyperscale — the core platform through which engineers and customers understand and act on CI results . In this high-impact position, you will own responsive web interfaces, API gateway architecture, and AI-assisted workflows that convert raw pipeline data into actionable signal, eliminating operational toil across our engineering organization. WHAT YOU'LL DO Build the Pipeline Visibility Layer: Architect and ship high-performance web applications using modern JavaScript/TypeScript frameworks to deliver real-time pipeline status, test results, and failure analytics at enterprise scale. Own Gateway & Backend Services: Develop and operate low-latency API gateways and backend microservices using typed systems languages (such as Go or Rust) to aggregate data across systems, enforce strict multi-tenant boundaries, and maintain reliable system contracts. Deliver Secure Access Controls: Implement robust authentication and authorization protocols (OAuth2/OIDC, SSO, RBAC) to ensure internal teams and external customers experience seamlessly scoped and fully audited access. Integrate AI Triage Workflows: Engineer retrieval-augmented generation (RAG) pipelines and LLM integrations over test metadata and failure logs to automate root-cause summaries, flak

About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team The AI team is focused on one of Stripe’s most strategic growth areas: enabling the monetization and scaling of AI-native and AI-enabled businesses. We’re in a unique position – partnering with the world’s most ambitious AI companies (the likes of OpenAI, Anthropic, NVIDIA, etc) building on the frontier of artificial intelligence -- across infrastructure, foundation models, agents, and applications -- to help them grow and commercialize globally using Stripe’s full financial stack. As part of Stripe’s GTM / Sales organization, this team works closely with Product, Engineering, and Marketing to shape Stripe’s AI GTM strategy and ensure that the world’s leading AI companies -- from early-stage innovators to the largest public players -- choose Stripe as their monetization platform. What you’ll do Work with existing Stripe customers in the AI Industry to develop and execute long-term sales strategies to expand Stripe’s revenue Own the full sales cycle, from business case development, to deal structuring and negotiating, to close Develop account plans and cross sell into your list of strategic AI customers, driving growth through expansion and new revenue streams Drive deal strategy and commercial negotiations for large, complex renewals Develop relationships with executive stakeholders within your book of business, deeply understanding problems they are solving and helping drive to solutions Be responsible for account mapping and coordinating ef

About the Role: Tubi's content platform is the engine behind one of the largest free streaming services in the world. Every play, every deal, every creator, every frame of video flows through systems CPE owns, and the surface area is enormous. Distributed services running on the hottest path of Tubi's traffic. Video pipelines processing one of the largest workloads in streaming. Workflow engines automating the operations that used to consume entire teams. Creator-facing products turning a back-office process into a real platform. And on top of all of it, an AI-native rebuild of the CMS that most companies aren't willing to attempt. This isn't a single-domain role. It's a platform where backend, frontend, video, infrastructure, and applied AI all collide at the scale where decisions actually matter, where an architectural choice ripples across millions of titles and billions of requests, and where the difference between "good enough" and "great" shows up in revenue. We're looking for builders who want to range across domains — backend one quarter, frontend the next, applied AI the one after that — and who want their work to be felt: by viewers when a title plays instantly, by creators when they go live the same day, by Content Ops when a workflow runs itself, and by the business when the platform stops being a cost center and starts being a force multiplier. The infrastructure is already there. The mandate is already there. What's missing is the people who want to build the thing, not talk about it. Come build it. This is a hybrid role based out of our Toronto office. You must be willing to travel to our Toronto office two days/week. What You'll Do: You'll work on systems that sit at the heart of Tubi's business, where the content pipeline meets the viewer, the creator, and increasingly, the AI agent. The work spans the full stack of a modern content platform: distributed services, video infrastructure, workflow automation, and applied AI, all running at

We're looking for an ML Data & Platform Engineer to own the infrastructure that powers our speech AI models: the pipelines that source and prepare training data, and the platform that trains, evaluates, and serves them in production. Speech AI has a data problem most ML teams don't, and you'll be at the centre of solving it, working as part of our ML team to remove friction across the entire lifecycle and get better models into production faster. This is a broad, cross-functional role suited to someone who enjoys working across the full stack: data infrastructure, distributed systems, and production ML, and who takes ownership of problems end to end rather than waiting to be told what to fix. What you'll do Designing, building, and maintaining scalable data pipelines for ingesting, transforming, validating, and storing large datasets used to train our models Developing and maintaining web scraping and data acquisition solutions to keep training datasets fresh, high-quality, and available at scale Building and operating the infrastructure that lets the ML team deploy and evaluate new models quickly, and that serves models efficiently and reliably in production Optimising infrastructure for both iteration speed and production reliability, including GPU utilisation, job scheduling, and training efficiency Implementing observability (monitoring, logging, alerting) across data pipelines and ML systems to catch issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with a solid backend or data engineering foundation Hands-on experience with containerisation and orchestration (Docker, Kubernetes), and working with a major cloud provider Experience building data pipelines and ETL/ELT processe

SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. ABOUT THE ROLE: The Sandbox service team at SpaceXAI builds and maintains a secure, scalable system that gives our models safe, controlled access to computational environments. This infrastructure powers critical workloads across training and product, enabling models to run code, build software, interact with tools, and even control applications with user interfaces. We provision containers and virtual machines on large-scale clusters, granting models interactive control over these remote environments. Our work spans the full stack: from orchestrating massive jobs and resource scheduling at the cluster level, to fine-tuning filesystem performance on nodes. The Sandbox service enables Grok to safely run and test code in real-time for user queries, and supports reinforcement learning in training, where models interactively explore tools ranging from compilers to productivity apps. BASIC QUALIFICATIONS: Expert knowledge of Rust, C++ or Go Familiarity with Python Deep experience with either Linux or Windows systems (familiarity with both is a strong plus) Experience with virtualisation and containerisation technologies (e.g., cgroups, KVM, gVisor, QEMU) Solid knowledge of the networking stack COMPENSATION AND BENEFITS: £107,000 -

NK Securities Research is a leading financial firm that leverages cutting-edge technology and sophisticated algorithms to trade the financial markets. Founded in 2011, we have gained invaluable experience in the field of High-Frequency Trading (HFT) across different asset classes. Role Overview We’re looking for engineers who can take AI work beyond experiments and make it hold up in production. You’ll work closely with quant researchers and infra engineers to build AI systems that actually get used improving research speed and internal tooling without slowing down the core stack. We value engineers who think about trade-offs, test what they build, and care about how things run in production. What You’ll Build Production AI Ship models that meet defined latency and reliability expectation Add monitoring, rollback, and guardrails before anything goes live Optimise inference across CPU/GPU environments when it matters Integration into Real Systems Plug AI into data-heavy workflows without hurting performance Work within existing low-latency architecture instead of fighting it Profile and remove bottlenecks rather than guessing AI for Engineers & Researchers Build tools that genuinely speed up research and development Improve code understanding, review workflows, and internal knowledge retrieval Keep systems auditable and predictable LLM & Retrieval Systems Implement structured RAG and embedding pipelines with validation in place Create safe integration layers between models and internal systems Performance & Standards Track latency, drift, and stability — not just accuracy Build observability into everything you ship Help raise the bar for how AI is engineered here What We’re Looking For Strong Python fundamentals Clear thinking around system design and performance trade-offs Experience deploying AI systems in production (1–5 years is typical) Familiarity with transformers, embeddings, or LLM deployment Nice to have: Exposure to C++ / Rust / Go E