Staff Network Engineer Jobs

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 FlashArray team to build high-performance, resilient storage software that powers mission-critical applications worldwide. As a core member of an agile engineering group, you will design and deliver zero-downtime algorithms that directly shape our enterprise storage and public cloud offerings. In this role, you will partner closely with global systems engineers and product teams to translate complex technical challenges into scalable, real-world solutions. Your work will directly impact how thousands of global enterprises manage, protect, and scale their data seamlessly. WHAT YOU’LL DO Design & Deliver Resilient Systems: Architect and implement high-performance algorithms for enterprise storage products, ensuring platform reliability, end-to-end delivery from concept to release, and six-nines availability. Expand Cloud Architecture: Extend core platform capabilities into public cloud environments (such as AWS), driving performance and agility for both traditional IT and cloud-native applications. Drive Problem Solving & Quality: Analyze and resolve complex systems software challenges, optimizing storage internals and contributing to continuous continuous platform upgrades. Collaborate & Mentor: Partner with multidisciplinary engineering peers to review code, refine architecture, and maintain high engineering standards across distributed software projects. WHAT YOU BRING Systems Programming Exp

About Hark Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory. We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world. To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems. About the Role At Hark, you'll lead the development for the foundation of agentic reinforcement learning (RL) environments: the digital worlds in which AI agents learn to use software, navigate complex workflows, and act on their surroundings. Our ambition is to simulate the breadth and complexity of the internet, connecting websites, apps, services, and artifacts into rich, stateful environments, with the potential to evolve dynamically over time. This is a chance to shape both the worlds agents learn in and the systems that make learning at scale possible. You'll build environments end-to-end and work closely with researchers to bring them into RL training and evaluation. You'll also develop the infrastructure behind our agent gym, making these environments easy to integrate, reproduce, and scale across heterogeneous execution targets. Responsibilities Build agent environments and simulations end-to-end, including frontend interfaces, backend services, APIs, data models, tools, and realistic workflows used to train and evaluate AI agents. Build the infrastructure that powers our agent gym, including orchestration, sandboxing, packaging, benchmarking, and systems for running environments across heterogeneous targets. Create interconnected simulations w

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. Responsible for managing the lifecycle of assigned part types, including technology transfers Technology transfer (between Taiwan and Virginia fabs), yield ramp, and process optimization. Conduct yield performance analysis and conduct yield variation analysis, understand top issues and any yield gap to business plan, point engineering teams to in the right direction to focus on for overall yield program improvement. Drive technology transfers and ensure alignment between sending and receiving high-volume manufacturing sites. Able to report yield metrics in various platforms. Able to source yield variations causes, and identify actions needed to resolve. Able to collaborate with other teams and work out the corrective and preventive actions on top yield loss paretos and factor into yield projection. Coordinate Technology transfer Nodes and Design ID Framework setup. Including but not limited to Design ID setup, process flow (traveler) setup and other related system setup (Q-time, SPC, GeRM, N2, LMS, attributes etc.) Coordinates targeting plan for lead lots. Supervise critical lead lots progress inline. Ramp-up collaboration with cross-functional teams to drive yield and quality improvements. Works closely with PEE, RDA, Metro, Change Control and QA teams to ensure smooth transfer and quality control. Driving conversion catch-up yield across Design IDs and coordinating cross-fab inline matching are. Develop and implement plans aiming to shorten yield learning cycle. Participates in PCRB meetings, SWAT conversions, and training workshops to align on business proc

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 FlashArray team builds the industry’s most innovative, high-performance, and highly available portfolio of products that are designed for the most demanding mission critical applications. While we deliver a hardware storage array, over 90% of our engineering staff are software engineers. Our customers are the most important part of our business and they love FlashArray for it's simplicity of management, the constant flow of new and exciting upgrades, and ability to live on the cutting edge of technology while never taking downtime, ever. Most recently, we extended the value of our FlashArray capabilities into the cloud with CloudSnap and Cloud Block Store for AWS. These new innovations enable our customers to leverage the agility of the public cloud for both traditional IT and cloud-native applications. What you'll be doing: Ensuring FlashArray and FlashBlade products and features ship with the highest quality. Create System test designs that focus on FlashArray and FlashBlade Find statistical and systemic hardware issues earlier in programs Design and build tools that make it efficient to execute and triage a large number of test scripts. Understand the inner workings of features to target specific failure domains in our stack to make the product/features fault tolerant and resilient. Continuously look for test gaps, perform root cause analysis & close loop on customer found defects. WHAT YOU’LL NEED T

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. About the role We are looking for an engineer to help build and scale diagnostic and manufacturing tooling for next-generation storage platforms. This role sits at the intersection of software, firmware, hardware bring-up, and factory validation. The focus is not generic application development; it is on building practical tools and workflows that improve debug velocity, manufacturing readiness, platform quality, and failure isolation across the product lifecycle. What you will do Design, implement, and maintain diagnostic and manufacturing tools used for bring-up, validation, failure analysis, and production workflows Develop software in C/C++ with Python-based automation where appropriate Build and improve tooling for inventory, platform configuration, health monitoring, event handling, firmware loading, and debug data collection Work across software and hardware boundaries to diagnose issues involving NAND, CPU, BMC, BIOS, EEPROM, PCIe, SAS, and board-level interfaces such as I2C, SMBus, and PMBus Partner with firmware and hardware teams during system power-on, platform bring-up, firmware management, and issue triage Improve testability, observability, and root-cause isolation in manufacturing and validation flows Translate recurring field, factory, or lab issues into robust tooling, checks, and debug workflows that reduce turnaround time Collaborate with global cross-functional teams to deliver production-quality solut

About Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! In September 2026 we raised a $350 million Series E at a $3.5 billion valuation , and we are scaling our engineering and research teams to meet demand. The role Frontier AI data is expensive to make and hard to measure. Every task we deliver is tested against the strongest models, often through many long-running agent rollouts. Your job is to make that process faster, cheaper, and more rigorous with ML and AI You will be one of the early members of ML & Research Engineering at Snorkel. You will study how frontier-grade data is generated and evaluated, form hypotheses, validate them against real production data, and ship the winners at scale. You will shape the discipline's direction, its standards, and the team that grows around it. What you'll work on Efficient agentic evals. Cut the cost of long-horizon agent evaluation with adaptive sampling, statistically grounded early stopping, model cascades, caching, and cheap-first gating. AI model routing. Route every eval and judge call to the cheapest model that clears the quality bar, with fallback, monitoring, and cost attribution. Fine-tuned small models. Fine-tune and serve open-weight models (LoRA and other parameter-efficient methods) where they match frontier quality, and know when they don't. Predictive difficulty. Build models that estimate how hard a task is for frontier systems before running a single rollout. Measurement for AI data. Build golden datasets, quantify the accuracy and calibratio

About the Team Consumer Monetization builds the experiences and systems that power how customers purchase and pay for OpenAI products. Our scope spans purchasing flows, payments, subscriptions, and billing, along with the shared capabilities that support new products, offers, and distribution channels. We own both customer-facing experiences and the underlying platforms that power them. We partner closely with Product, Design, Growth, Data Science, and engineering teams across OpenAI to make purchasing effective and reliable, and new offerings easier to launch and monetize. About the Role We’re looking for experienced Staff+ engineers with deep iOS or Android expertise and demonstrated experience contributing beyond mobile to frontend web or backend development. You’ll shape and build purchasing and subscription experiences across mobile applications, web, and supporting product APIs. The work spans improving conversion, performance, and reliability, building reusable components, and enabling new product launches. You’ll combine product judgment with technical depth to set direction, lead initiatives across teams, and remain hands-on through implementation and delivery. Approximately 50% of the work will initially be native mobile development, with the remainder across web and product APIs. We’re looking for engineers who enjoy working across the stack and have concrete examples of doing so professionally. Depth in either iOS or Android is required; experience in both is not required. This role is based in San Francisco, with three days per week in the office. In this role, you will: Architect and build purchasing and subscription experiences across native mobile, mobile web, and supporting product APIs. Partner with Product, Design, and Growth to identify customer needs, prioritize improvements, and shape technical direction for purchasing experiences. Improve conversion, performance, and reliability through experimentation, product analytics, and customer insights

About the Team Consumer Monetization builds the experiences and systems that power how customers purchase and pay for OpenAI products. Our scope spans purchasing flows, payments, subscriptions, and billing, along with the shared capabilities that support new products, offers, and distribution channels. We own both customer-facing experiences and the underlying platforms that power them. We partner closely with Product, Design, Growth, Data Science, and engineering teams across OpenAI to make purchasing effective and reliable, and new offerings easier to launch and monetize. About the Role We’re looking for experienced Staff+ full-stack engineers to shape purchasing experiences and monetization capabilities across OpenAI products. You’ll work across web experiences, product APIs, and shared components, combining strong product judgment with technical depth. The work ranges from improving checkout conversion and performance to enabling new pricing models, offers, and ways for customers to purchase our products. You’ll help identify opportunities, turn ambiguous goals into concrete technical plans, and lead initiatives from exploration through launch. As patterns emerge across products, you’ll develop reusable capabilities that make future launches faster and more consistent. This is a hands-on technical leadership role with substantial ownership over architecture, implementation, and product outcomes. This role is based in San Francisco, with three days per week in the office. In this role, you will: Architect and build purchasing experiences end to end, from frontend interactions through the product APIs and backend integrations that support them. Improve checkout conversion, performance, and reliability through experimentation, product analytics, and customer insights. Develop shared checkout components and monetization capabilities that support new products, pricing models, offers, and distribution channels. Partner with Product, Design, Growth, and Data Science to

NOTE ON ELIGIBILITY : this role is for industry candidates already working in software engineering for the past 1+ years. New grads and current students should NOT apply to this role (instead, visit pplx.ai/newgrads ). About the role In 2026, we launched Computer, the defining product for the new era of agentic AI. Millions of people now use Perplexity to transform knowledge into action, through Search, Comet, and Computer. Product Engineering owns those experiences end to end, from the interface a user touches to the search and model calls behind it, on top of roughly 200M queries a day and a 200B+ URL index. This is a full stack role in the truest sense. There are no frontend or backend lanes here, and no one waits for a ticket before starting. It suits people who love to build and want the room to do it. What you have shipped and how quickly you learn tell us far more than years on a resume or where you went to school. What you will do Own a user-facing surface end to end, from the interaction, design, and frontend through the application logic, APIs, data models, and the search or model calls behind them. Ship to production continuously. New engineers typically have code in front of users in their first week. Build with AI as a primary tool, not a novelty. We expect you to use agents and coding models aggressively to move faster, and to have sound judgment about when their output is wrong. Turn ambiguous product ideas into working prototypes, then use real usage data and evals to decide what survives. Raise the quality bar on reliability, latency, and craft for the surfaces you own. Work directly with designers, PMs, and research engineers, without unnecessary process in between. What we look for 1+ years of professional software engineering experience, or a track record of strong internships where you shipped production code at scale. Strong in at least one backend language, such as Python or Go, with real experience across services, APIs, and relational data m

The Chief of Staff is a high-visibility leadership role responsible for the operational success of the SVP, General Manager’s organization. Acting as a strategic partner, you will ensure the business operates at peak velocity by driving key initiatives, improving organizational effectiveness, and aligning teams around strategic priorities. You will translate high-level goals into clear execution plans across the business, providing clarity in a fast-paced, complex environment. This role is designed for a seasoned operator who excels at driving accountability among senior leaders and building the systems necessary to scale a SaaS business. You will identify operational gaps, mitigate risks before they impact the bottom line, and act as an extension of leadership—prioritizing, executing, and communicating critical projects to enable the executive team to operate at maximum efficiency. ** Please note that this opportunity is located in New York, NY, and requires this hire to work from our office 5 days a week. ** As a Chief of Staff You Can Expect: Drive the Operating Cadence: Design and manage the GM's leadership meetings, QBRs, and planning cycles to keep the team aligned and hitting KPIs. Force Execution: Partner with Sales, Customer Success, Account Management, Implementation, Product leaders to unblock projects and hold senior stakeholders accountable for deliverables. Own Plan Integrity: Keep the team aligned to the quarterly and annual plan, hold leaders accountable for the commitments behind it, and flag risk early, before it shows up as a miss in the business review. Drive Alignment & Follow-Through: Ensure decisions and commitments made in leadership meetings are clearly communicated across the team, tracked to completion, and actually followed through on, closing the loop between what was decided and what gets done. Prioritize Ruthlessly: Act as a surrogate for the GM in high-level meetings, surfacing critical risks early and filtering out noise to focus

Perplexity is seeking an experienced Machine Learning Research Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. Responsibilities Relentlessly push search quality forward — through models, data, tools, or any other leverage available Architect and build core components of the search platform and model stack Design, train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models Conduct advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval Deploy models — from boosting algorithms to LLMs — in a scalable and performant way Build and optimize RAG pipelines for grounding and answer generation Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery Qualifications Deep understanding of search and retrieval systems, including quality evaluation principles and metrics Proven track record with large-scale search or recommender systems Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR) Self-driven, with a strong sense of ownership and execution Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas

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. Micron in Boise, Idaho has a current need for a Senior or Staff R&D Photolithography Materials Engineer . 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. As an R&D Photolithography Materials Engineer at Micron Technology Inc., we are responsible for photolithography material selection and optimization for advanced memory devices. In this position, you will develop new technology and photo materials that meet device requirements and integrate new technology into manufacturing processes. This work could include photo materials for any photo processes including hardmasks and resists for I-line (365nm) through EUV (13.5nm). Additionally, you will address process and materials issues with pilot manufacturing and work on continuous improvements for manufacturability. Job Responsibilities: Lead long-term resist screening projects. Define success criteria with process owners, evaluate multiple rounds of design of experiments (DOEs) with suppliers, and identify top materials for detailed evaluation on devices in technology development. Broad knowledge and experience in photoresist chemistry with an ability to direct material optimization and development with our suppliers Work in a cleanroom, follow safety proc

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You'll set technical direction for the primitives that other teams (filesystems, training, sandboxes) build on, balancing durability, latency, throughput, and cost. You'll own the roadmap from today's hardest problems (garbage collection at petabyte scale, active-active replication, rate limiting that protects the upstream without wasting ut

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Specifically, you'll be working on Modal's machines layer: the fleet of bare metal and cloud hosts that every Function, Sandbox, and training job runs on, and the control plane that provisions, images, monitors, and repairs them. You'll automate the integration of new capacity from a growing set of hardware providers; from auditing and benchmarking hosts and clusters, to maintaining our machine images, configuring GPUs, RDMA, networking, and storage, and getting machines into production. You'll build the automation that keeps the fleet healthy without human intervention: detecting bad GPUs, thermals, and disks. You'll dig into whatever is between the hardware and the software that runs on top of it, whether that is a kern

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Specifically, you'll be working on the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You'll make cold starts feel local when the data is hundreds of milliseconds away, designing the caching, preloading, and peer-to-peer layers that hide object-store latency and keep public ingress off saturated uplinks. You'll own durability and cost at petabyte scale, from streaming and batch replication between origins, to garbage collection over billions of objects. You'