Staff Senior Emission Analyst 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 Team lead for a group of engineers with responsibility for Linux and VMWare initiator stack behavior as well as Fibre Channel and NIC drivers on Pure storage array. WHAT YOU'LL DO The Kernel and Driver development team is responsible for several areas Working for a team that deals primarily in various storage area network protocols such as Fiber Channel and Ethernet. On the initiator side, the team will be responsible for Linux initiator behavior attached to Flasharray. Focus is on NVME (ROCE, FC, TCP/IP) but also includes FC-SCSI (FCP) and iSCSI interfaces. This includes software development/fixes for Linux initiator stack, debugging initiator problems, and creating compatibility documents for Purity. For VMWare, focus is on debugging issues with VMWare as an initiator, and creating compatibility documentation. For FibreChannel and NIC Drivers, focus is responsibility for FC-SCSI driver stack for storage side and responsibility for NIC drivers on networking side. This includes responsibility for related code, utilities, enhancements supporting RAS, as well as debugging failures found internally and in the field. Maintain Linux kernels for internal testing Documenting supported configurations for customers Responsibility for evaluating Linux initiator behavior and optimizing for Pure Storage Flasharray. This includes correctness as well as optimizing for performances. Will contribute bug fixes and enhancemen

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. THE ROLE Team lead for a group of engineers with responsibility for Linux and VMWare initiator stack behavior as well as Fibre Channel and NIC drivers on Pure storage array. WHAT YOU'LL DO The Kernel and Driver development team is responsible for several areas Working for a team that deals primarily in various storage area network protocols such as Fiber Channel and Ethernet. On the initiator side, the team will be responsible for Linux initiator behavior attached to Flasharray. Focus is on NVME (ROCE, FC, TCP/IP) but also includes FC-SCSI (FCP) and iSCSI interfaces. This includes software development/fixes for Linux initiator stack, debugging initiator problems, and creating compatibility documents for Purity. For VMWare, focus is on debugging issues with VMWare as an initiator, and creating compatibility documentation. For FibreChannel and NIC Drivers, focus is responsibility for FC-SCSI driver stack for storage side and responsibility for NIC drivers on networking side. This includes responsibility for related code, utilities, enhancements supporting RAS, as well as debugging failures found internally and in the field. Maintain Linux kernels for internal testing Documenting supported configurations for customers Responsibility for evaluating Linux initiator behavior and optimizing for Pure Storage Flasharray. This includes correctness as well as optimizing for performances. Will contribute bug fixes and enhancemen

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

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 Team lead for a group of engineers with responsibility for Linux and VMWare initiator stack behavior as well as Fibre Channel and NIC drivers on Pure storage array. WHAT YOU'LL DO The Kernel and Driver development team is responsible for several areas Working for a team that deals primarily in various storage area network protocols such as Fiber Channel and Ethernet. On the initiator side, the team will be responsible for Linux initiator behavior attached to Flasharray. Focus is on NVME (ROCE, FC, TCP/IP) but also includes FC-SCSI (FCP) and iSCSI interfaces. This includes software development/fixes for Linux initiator stack, debugging initiator problems, and creating compatibility documents for Purity. For VMWare, focus is on debugging issues with VMWare as an initiator, and creating compatibility documentation. For FibreChannel and NIC Drivers, focus is responsibility for FC-SCSI driver stack for storage side and responsibility for NIC drivers on networking side. This includes responsibility for related code, utilities, enhancements supporting RAS, as well as debugging failures found internally and in the field. Maintain Linux kernels for internal testing Documenting supported configurations for customers Responsibility for evaluating Linux initiator behavior and optimizing for Pure Storage Flasharray. This includes correctness as well as optimizing for performances. Will contribute bug fixes and enhancemen

Perplexity is seeking a DevOps engineer to join our small team in revolutionizing the way people search and interact with the internet. You will be responsible for leading the design, implementation, and scaling of the infrastructure, tooling and systems that support our Search feature. The ideal candidate should have experience in designing highly scalable infrastructure, building systems, tools and performing testing, monitoring, and maintenance. Our backend stack includes Python, Go, Rust, PostgreSQL, DynamoDB, Redis, and Kubernetes - built alongside dedicated in-house AI and search interfaces. Responsibilities: Design and implement highly available, high-performance, and scalable systems. Design and implement various tools for other engineers helping them with their day to day job. Design and implement CI/CD and other workflows to enhance stability, robustness and speed of iteration. Maintain and optimize key-value and relational databases. Scale and load balance web server backends to meet rapidly changing needs. Monitor systems and applications, proactively identifying and resolving reliability, scalability, or performance issues. Develop monitoring tools, alerts, and dashboards to provide visibility into system health and performance. Qualifications: Strong experience with cloud infrastructure built on AWS. Proficient in database management and caching strategies. Excellent problem-solving and troubleshooting skills, with the ability to analyze, debug, and resolve complex technical issues. Experience working with containers (Docker, Kubernetes) and orchestration tools. Excellent communication and collaboration skills. Experience with Python and Terraform. 3+ years of DevOps/SRE/System Administration experience.

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

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. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Member of Technical Staff (MTS) – 3D NAND Process Integration Job / Role Summary The role drives advanced 3D NAND technology pathfinding and process integration within the Advanced NAND Technology team in Singapore. It leads the translation of device concepts, materials, and process architecture into validated, manufacturable technology solutions. The position coordinates end-to-end development from concept through technology validation, applies data-driven, AI-assisted, and model-based methods to resolve complex scaling challenges, and collaborates across Device, Probe, Test, Design, and Operations to improve performance, yield learning, execution, and future-node roadmap readiness. Main Responsibilities Lead 3D NAND integration, pathfinding, and advanced architecture enablement in support of the technology roadmap. Drive end-to-end technology development from concept through validation, including learning vehicles, chara

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 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 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 own the full lifecycle of a machine, from accepting and benchmarking new hardware from a growing set of providers, to network bring-up, kernel and image management, GPU and disk health tracking, and automated remediation of unhealthy hosts. You'll manage a team of 3–8 engineers while staying hands-on across the stack which involves BMCs, firmware, PXE, bootloaders, Linux networking, drivers, and distributed control-plane services, and you'll shape our long-

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'