Network Engineer Jobs

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems. About the Role EnCharge AI is seeking a highly skilled and experienced AI Compiler Engineer to spearhead the efforts in developing and optimizing graph compilers tailored to cutting-edge AI and ML workloads. You will collaborate with hardware architects, and AI researchers to enhance performance, optimize computation graphs, and enable efficient model deployment on EnCharge’s Inference Accelerators. Responsibilities Architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization. Work closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges. Work on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations. Develop compiler optimizations and passes that convert high-level AI models (e.g., from TensorFlow, PyTorch) into intermediate representations (IR). Implement parsing, semantic analysis, and IR generation for deep learning frameworks. Research and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers. Provide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations. Qual

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, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Fraud Data team at Plaid builds the machine learning systems that power Plaid’s fraud detection products, leveraging insights from across Plaid’s network to help identify and stop fraud before it happens. Our team works across the full data science and machine learning lifecycle—from discovering new signals and experimenting with models to deploying and optimizing them in production. We continuously learn from real-world model performance and customer feedback to improve our systems and develop new ways to protect customers and consumers from evolving fraud threats. As a Senior Machine Learning Engineer on Plaid's Fraud Data team, you will develop models that improve fraud detection for our customers. You will identify predictive patterns in Plaid's network data and lead projects from initial experiments through model deployment and ongoing improvement. Investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage across customers and use cases. Develop training datasets and predictive features, addressing challenges such as incomplete labels, class imbalance, data leakage, and changing fraud behavior. Design, train, and tune model

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, Seattle, Washington D.C., Raleigh, London, and Amsterdam. We are the Fraud Data team within Plaid's Fraud organization. We leverage relationships and activity across Plaid’s network to develop machine learning models that help customers detect and prevent fraud while minimizing friction for legitimate users. Our team owns the full model lifecycle, from data processing and experimentation to feature pipelines, model serving, and monitoring. In this research role, you’ll partner closely with Machine Learning Engineers and Data Scientists to uncover new signals, explore and evaluate innovative modeling approaches, and translate promising research into production-ready solutions that strengthen fraud detection across Plaid’s network. As a Senior Research Scientist, you will lead applied research to develop next-generation fraud detection models across relational graphs, sequential events, images, and video data. You’ll design rigorous experiments and evaluation methodologies that reflect real-world fraud dynamics, while exploring state-of-the-art approaches such as Graph Neural Networks and Transformer-based foundation models. In close partnership with Machine Learning Engineers, you’ll translate promising research into production-ready solu

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, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. The team owns the full ML lifecycle—from feature pipelines and model training to production serving and monitoring—building reliable, scalable systems that deliver high-quality fraud detection as we grow to support hundreds of customers. As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale. You’ll build robust observability, monitoring, and automated debugging capabilities, while leveraging AI-assisted tools to investigate complex system behavior and maintain high reliability. You’ll partner closely with ML Infrastructure, Data Science, and Product teams to execute critical technical initiatives and deliver scalable, high-impact ML solutions. Responsibilities: Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment. Solve complex technical challenges at the intersect

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, Seattle, Washington D.C., Raleigh, London, and Amsterdam. About the Team The Embedded Insights team supports Plaid’s mission to build a world-class suite of intelligence products. We identify the best opportunities to use machine learning in Plaid products, prove out those opportunities, and collaborate with cross-functional partners to turn them into real-world production systems. About the Role As a Senior Machine Learning Engineer on Embedded Insights, you will help shape Plaid’s future by building machine learning-powered products and features. You will initially support the Plaid App, working on a 0-to-1 consumer-facing product and helping establish product-market fit for a new business line. You will work closely with product managers, data scientists, engineers, customers, and other machine learning engineers to translate ambiguous opportunities into effective ML systems that create measurable customer value. In supporting the Plaid App, you will: Build machine learning-based features for a 0-to-1 consumer-facing product. Partner with product managers to translate ambiguous business requirements into machine learning problems and influence product strategy and roadmap decisions. Rapidly iterate and experiment to help drive product

About Supabase Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth. Start in minutes with a fully managed, scalable Postgres database and a suite of tools that eliminate the complexity of backend development. Use one or use all—Supabase gives teams the flexibility of open source with the speed and simplicity of a modern platform, so they can move fast without sacrificing control. What Are We Looking For We’re looking for a Platform Security Engineer to join our team and help strengthen the security of Supabase’s infrastructure, cloud platform, Kubernetes environments, and containerized workloads as we continue to scale. You’ll work closely with infrastructure, platform, SRE, product engineering, and technical leadership , helping us proactively reduce risk across the systems that run Supabase. This role is ideal for someone who has deep hands-on experience with AWS, Kubernetes, containers, Linux, and large cloud environments and is excited about securing developer infrastructure without slowing teams down. Success in this role means improving the security posture of the platform through pragmatic engineering, clear technical judgment, and scalable guardrails. What You’ll Be Responsible for In this role, you’ll: Identify and reduce security risk across AWS, Kubernetes, containerized workloads, and platform infrastructure. Conduct threat modeling, architecture reviews, and technical risk assessments for platform and infrastructure systems. Partner closely with infrastructure, platform, and SRE teams to design practical controls and secure-by-default patterns. Improve Kubernetes and container security across areas like workload isolation, RBAC, admission control, secrets, network policy, and runtime hardening. Assess container runtime and Linux isolation risks, in

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role Operating a vehicle remotely over cellular networks is challenging and critical. You will be responsible for ensuring that our "eyes on the road" never blink. You’ll tackle deep-stack networking challenges—from bonding multiple LTE carriers to designing custom FEC (Forward Error Correction) algorithms that out-perform standard protocols. About the Work Engineered Connectivity: Architect a network bonding framework to aggregate bandwidth across multiple cellular providers (Verizon, AT&T, T-Mobile) to ensure zero-drop connectivity. Performance Modeling: Build sophisticated ns-3-like simulations to "stress test" our stack against edge cases like tunnel entries, rural dead zones, and network congestion. Optimization: Develop and implement custom congestion control algorithms specifically tuned for high-bitrate, low-latency video streaming. Cross-Functional Leadership: Partner with Hardware and Embedded teams to optimize the netw

Senior Cloud Security Engineer At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI , Uber , Spotify , Instacart , Cruise , and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. About the Role Anyscale's security needs are growing as we operate more production and cloud infrastructure for larger and more demanding customers. We're looking for a Senior Cloud Security Engineer to own the security of that infrastructure. This is a hands-on, high-ownership role: you will own how our production and cloud environments are hardened, isolated, and monitored. You will set and drive the direction for infrastructure and production security, reporting to the Head of Security and partnering closely with the wider engineering organization. This role is based in the San Francisco, Bay Area. In your first year, success looks like hardened and well-segmented production environments, strong runtime security coverage across our container footprint, and a clear, defensible story for how we secure the infrastructure our customers rely on. What You'll Do Own the security posture of Anyscale's production and cloud infrastructure across AWS and Azure, including hardening, network segmentation, and tenant isolation. Own runtime security coverage across our Kubernetes environments, from deployment through detection of anomalous activity. Partner with engineering on secure infrastructure architectur

We are looking for a Senior System Software Engineer, Software Defined Networking to design, build, and operate highly performant and scalable SDN solutions for NVIDIA's AI Clouds hosting GPU-accelerated workloads — including hyperscale multi-node training, inference, cloud gaming, and cloud functions. This role spans the full lifecycle of our SDN stack — from designing and developing new control and data plane software to ensuring operational excellence in production through reliability engineering, CI/CD, observability, and incident response. What you'll be doing: Design and develop next-generation multi-tenant cloud SDN control and data plane software (OVS, OVN, OpenFlow) Build Infrastructure-as-a-Service virtual network orchestration and services using gRPC and REST to support tenant workload security and performance SLAs for BMaaS, VMaaS, and Kubernetes Drive upstream contributions to OVN-Kubernetes and related open-source projects Develop software for network observability — monitoring, telemetry, intelligent metering, and performance analysis Operate and support OVS-OVN based SDN solutions in large-scale NVIDIA AI Cloud environments Own end-to-end observability for the SDN stack — build and maintain monitoring, alerting, distributed tracing, and dashboarding to ensure real-time insight into network health, performance, and tenant SLAs Design, enhance, and maintain CI/CD pipelines (GitLab) across Linux host networking, OVS, OVN, and Kubernetes CNIs Implement GitOps approaches or related experience for secure, seamless integration with cloud infrastructure Drive reliability through incident management, resource monitoring, and performance tuning<

Job Title Field Service Engineer - Medical Imaging (Metro NYC / Tri-State Area) Job Description Field Service Engineer - Medical Imaging (Metro NYC / Tri-State Area) You will be responsible for customer relationship management through the effective use of technical knowledge to install, troubleshoot, service, and maintain equipment at customer sites nationally. The ideal candidate will be able to travel nationally but will be located in the greater New York City area. We expect this FSE to live within a 45-minute commute to Manhattan, with travel to other sites as required. Your role: Deliver exceptional customer service by setting clear expectations, meeting commitments and arrival times, and resolving customer issues efficiently. Build strong customer relationships by understanding their business environment while contributing to service revenue growth. Collaborate as an active member of the regional team to improve processes, identify training gaps, and share best practices. Continuously expand capabilities by learning new tools and becoming qualified across multiple modalities as needed. Complete all required administrative and compliance tasks in accordance with company, state, and federal regulations. This includes accurate documentation for timesheets, service work orders, expense reports, PMs, FCOs, installations, and adherence to quality and safety standards. Diagnose and resolve electronic, mechanical, and network-related system issues using established tools, resources, and escalation processes. Perform preventative maintenance, field changes, installations, and corrective service within required timeframes. Travel extensively (approximately 90%) with the ultimate focus being in the assigned territory to support customer sites, but will require overnight stays and air travel nationwide

We are looking for an innovative thermal solutions integration Engineer. NVIDIA offers you to be a part of the System Product Engineering group and be responsible for assuring the best quality products to be a sale and deliver to NVIDIA’ s customers. The job provides deep knowledge of NVIDIA systems, a system-level view of our solutions, and a dynamic and positive working environment and offers the candidates the opportunity to take a major role in our testing strategy by leading our thermal solutions integration and testing for the company production. NVIDIA Networking unit has continuously reinvented itself over two decades. Our high-speed buses & network products are leading in the markets with innovative ways to improve speed and bandwidth from one generation to another. Today, we are increasingly known as the place for getting “End-to-End High-Speed Ethernet and InfiniBand Solutions” We're looking to grow our company and build our teams with smart people who can join us at the forefront of technological advancement. If you are passionate about enabling the highest quality Network products that will change the world, we want to hear from you! What You’ll Be Doing: Integration and testing of next-generation, large-scale thermal, pressure, and liquid management solutions. Perform qualification tests for cutting edge cooling and sensing solution. Analyse and summarize thermal performance data and fluid dynamics results to support design reviews and decision-making. Primary onsite focal point for malfunctions in thermal liquid cooling stations. Diagnose and resolve hardware/software issues to maintain continuous development labs activity Maintain and update hardware (manifolds, connectors, sensors) and software versions across all thermal systems according to engineering specifications. Perform initial RCA on thermal system failures. Extract detailed fail reports and corrective/

Location Details: At GoDaddy the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) and some work entirely remotely.​ Remote: This is a remote position, so you’ll be working remotely from your home. You may occasionally visit a GoDaddy office to meet with your team for events or meetings. About the Team Global Compute builds and operates the core cloud infrastructure that engineering teams rely on every day. We provision and manage AWS accounts across the company, operate the network backbone that connects them, and maintain the security guardrails that keep those environments safe, compliant, and scalable. We believe reliability is an engineering challenge, not an operations task. We automate repetitive work, build for scale before it becomes a problem, and invest heavily in observability to identify issues before they impact the business. What you'll get to do... Operate and scale AWS production infrastructure, owning the health of services that provision, secure, and manage accounts across GoDaddy AWS organisations. Design, build, and maintain cloud platform capabilities using Python, CloudFormation, AWS CDK, and automation-first practices. Drive cost optimisation initiatives that improve efficiency and deliver measurable business impact. Improve observability through monitoring, alerting, dashboards, and operational tooling. Participate in on-call rotations, lead incident response efforts, and drive long-term reliability improvements through blameless post-incident reviews. Support strategic AWS initiatives across networking, identity, governance, and multi-account architecture. Review code and designs, contribute documentation and operational runbooks, and mentor fellow engineers. Leverage AI-assisted tooling to improve engineering productivity, accelerate automation, and reduce operati

Location Details: At GoDaddy, the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) and some work entirely remotely.​ Remote: This is a remote position, so you’ll be working remotely from your home. You may occasionally visit a GoDaddy office to meet with your team for events or meetings. About the Team Global Compute builds and operates the core cloud infrastructure that engineering teams rely on every day. We provision and manage AWS accounts across the company, operate the network backbone that connects them, and maintain the security guardrails that keep those environments safe, compliant, and scalable. We believe reliability is an engineering challenge, not an operations task. We automate repetitive work, build for scale before it becomes a problem, and invest heavily in observability to identify issues before they impact the business. What you'll get to do... Operate and scale AWS production infrastructure, owning the health of services that provision, secure, and manage accounts across GoDaddy AWS organisations. Design, build, and maintain cloud platform capabilities using Python, CloudFormation, AWS CDK, and automation-first practices. Drive cost optimisation initiatives that improve efficiency and deliver measurable business impact. Improve observability through monitoring, alerting, dashboards, and operational tooling. Participate in on-call rotations, lead incident response efforts, and drive long-term reliability improvements through blameless post-incident reviews. Support strategic AWS initiatives across networking, identity, governance, and multi-account architecture. Review code and designs, contribute documentation and operational runbooks, and mentor fellow engineers. Leverage AI-assisted tooling to improve engineering productivity, accelerate automation, and reduce operat

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

NVIDIA is hiring an NCX Senior Engineer who is passionate about NVIDIA Cloud Partner (NCP) infrastructure operations to join our DSX team. This role involves working closely with strategic NVIDIA Cloud Partners to build and improve the operational capabilities essential for running large-scale NVIDIA accelerated infrastructure reliably in production. Your role involves guiding partners beyond the initial cluster deployment and validation phase into advanced Day 2 operations. These operations cover ongoing infrastructure health, observability, lifecycle management, quick remediation, performance validation, and operational readiness. You will engage directly with partner engineering and operations teams to develop consistent approaches that support NVIDIA workloads and the broader external customer environments of the partners. This is a highly technical, hands-on role at the intersection of NVIDIA accelerated computing, cloud infrastructure, distributed systems, and production operations. What you'll be doing: Lead NCP Day 2 operational readiness efforts. Collaborate directly with NVIDIA Cloud Partners to set up the systems, procedures, automation, and operational methods necessary to consistently manage NVIDIA accelerated infrastructure following initial deployment and activation. Build continuous infrastructure validation. Develop and implement methods to continuously validate GPU, CPU, storage, and network health. Do this across large-scale AI clusters to identify degraded infrastructure before it impacts critical training or inference workloads. Establish observability and operational telemetry. Help NCPs implement comprehensive telemetry, monitoring, alerting, dashboards, and operational signals across compute, GPU, InfiniBand/RoCE networking, storage, Kubernetes, and AI workloads. Devel