Node Development Product Engineer Jobs

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: Most of the value of owning a model shows up at serving time. We're building a platform that covers the whole life of an LLM -- train it, deploy it, observe it -- and inference is where teams feel the difference every day. We already run elastic inference, sandboxes, distributed volumes, and multi-node training, and we control the infrastructure underneath, so the serving stack is ours to shape rather than something we resell. You will do hands-on inference research at Modal, working with the research lead to pick high-impact bets and owning them end to end. The bets that matter most are the ones that move cost per token and tail latency on the workloads our customers actually run. What You'll Do: Own end-to-end inference research bets: speculative decoding, disaggregated prefill/decode, quantization (FP8, INT4), KV-cache and memory management, autoscaling for spiky serverless traffic, and whateve

About the Team The Spark Platform team owns and operates DoorDash's Apache Spark ecosystem — the execution runtime, remote shuffle service, cluster scheduler, and reliability tooling that powers the company's data, analytics, and ML workloads. We run Spark across the company at significant scale and continue to expand the workloads, capabilities, and consumer base we serve. Orchestrating and operating thousands of Spark cluster deployments is a complex distributed system problem which the team invests heavily in runtime optimization, systems architecture, multi-tenant scheduling, and end-user tooling. About the Role As a Software Engineer on Spark Platform, you will execute across the surfaces of our in-house Spark deployment that serves the entire company. The work spans Spark runtime upgrades and performance, multi-tenant scheduling and executor bin-packing on Kubernetes, cluster lifecycle automation, and the observability and incident automation that keep the platform sustainable. You will move between layers as the work demands — picking up the next high-leverage problem regardless of where it sits — and partner closely with the rest of the team and with platform consumers across the company. You must be located in San Francisco, Sunnyvale, Seattle, or New York City for this hybrid position. You will report into the Engineering Manager on our Spark Platform team. You're excited about this opportunity because you will… Build and operate an in-house Spark platform that runs at company-wide scale, spanning runtime, scheduler, reliability, and user-facing tooling. Drive multi-tenant scheduling, executor bin-packing, and cost-aware placement that let a small team serve dozens of consumer teams. Own pieces of cluster lifecycle automation — provisioning, upgrades, capacity changes, and node-failure handling — at a scale where these stop being manual events. Build the observability and incident automation that make the platform debuggable end-to-end and keep on-call sus

Who We Are Verkada is transforming how organizations protect their people and places with an integrated, privacy-sensitive AI-powered platform that includes solutions for video security, access control, air quality sensors, alarms, intercoms, and visitor management. We’ve got serious momentum in the market: more than 30,000 customers (including 100+ of the Fortune 500), a $5.8B valuation , more than $1 billion in annualized bookings, and backing from CapitalG, Sequoia Capital, General Catalyst, Felicis Ventures, Next47 and more. Physical AI is one of the most consequential technology shifts of our time, and Verkada is at the center of it. You can look at all kinds of communities to see our platform’s impact in the world. It's the retailer that uses our agentic AI to deter theft before it happens. The warehouse that uses AI-powered alerts to make sure its team is protected on the floor with proper PPE. The school that’s alerted to a threat in real-time and triggers a lockdown in seconds, not minutes. We’re rapidly scaling this impact: today, more than 2 million Verkada devices are deployed across 170+ countries. About the Role We are looking for a seasoned Systems level Linux engineer / Linux internals expert to help us build our next generation of command connector product that turns any legacy camera into a cloud-native, AI-searchable node. Under the hood, it's managing dozens of concurrent video streams, translating disparate camera protocols into a single unified pipeline, and feeding all of it into CV models / inference run at the edge of the device itself. We currently support 100K+ customer cameras on this product and looking for more technical talent to help our growth. What You'll Do Reading kernel oops/panic traces Debugging GPU driver issues Working with boot/security subsystems like secure boot and luks disk encryption Networking internals (routing, firewalling, link-layer issues) RAID array management (mdadm, disk failure/rebuild

P-1930 At Databricks, we run the world's largest AI platform. We believe the next breakthroughs will come from models trained on domain-specific enterprise data, not from bigger general-purpose models alone. That's why we built AI Runtime , a serverless product that makes GPU training of any size feel simple, so companies can train their own models the way frontier labs do. Customers across industries already use it to train models on governed data, from automotive AI at Rivian to Merck's TEDDY foundation models for single-cell biology and drug discovery, all the way to agents like KARL from Databricks AI Research. It takes a lot of engineering to make that work, and we've written about it on our blog: how we keep GPUs reliable , make training fault-tolerant , and speed up fine-tuning with fused kernels and smart sequence packing. As a Staff Software Engineer, you'll work on the systems behind all of this, solving hard technical problems and shaping major parts of AI Runtime. The impact you will have: Drive major parts of AI Runtime's architecture and help shape Databricks' long-term technical direction in AI training infrastructure. Design the reliability and observability systems for a fleet that keeps growing in scale and complexity. Lead improvements to AI Runtime's developer experience, so the largest training jobs feel as simple as a first experiment. Help choose the technical bets that prepare AI Runtime for new GPU generations and workload types. Take on the hardest multi-node training problems yourself, from orchestration and GPU scheduling to data loading and checkpointing. Make training faster and cheaper by raising GPU efficiency across model architectures and hardware generations. Work with research, product, and customers to shape what we build next and lead projects across teams. Champion engineering excellence, raise the bar, and mentor the engineers around you. What we look for: 10+ years in large-scale distributed systems, with significant depth in

P-1428 At Databricks, we run the world's largest AI platform. We believe the next breakthroughs will come from models trained on domain-specific enterprise data, not from bigger general-purpose models alone. That's why we built AI Runtime , a serverless product that makes GPU training of any size feel simple, so companies can train their own models the way frontier labs do. Customers across industries already use it to train models on governed data, from automotive AI at Rivian to Merck's TEDDY foundation models for single-cell biology and drug discovery, all the way to agents like KARL from Databricks AI Research. It takes a lot of engineering to make that work, and we've written about it on our blog: how we keep GPUs reliable , make training fault-tolerant , and speed up fine-tuning with fused kernels and smart sequence packing. As a Senior Software Engineer, you'll work on the systems behind all of this, solving hard technical problems and helping shape how AI Runtime evolves. The impact you will have: Build core parts of AI Runtime and contribute to Databricks' technical direction in AI training infrastructure. Develop reliability and observability features for a fleet that keeps growing in scale and complexity. Improve AI Runtime's developer experience, so the largest training jobs feel as simple as a first experiment. Help prepare AI Runtime for new GPU generations and workload types. Take on hard multi-node training problems, from orchestration and GPU scheduling to data loading and checkpointing. Make training faster and cheaper by raising GPU efficiency across model architectures and hardware generations. Work with research, product, and customers on what we build next and own features from design through production. Champion engineering excellence, raise the bar, and help the engineers around you grow. What we look for: 5+ years in large-scale distributed systems, with experience in GPU training, high-performance computing, or ML systems. Experience traini

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. Responsibilities and Tasks Join our IE Modeling team at Micron and be a key player in driving our success in the semiconductor industry. You will give to daring projects that push technology and innovation boundaries. Apply industrial engineering concepts to model capacity thresholds, ensuring detailed support for both existing and future technologies. Lead capacity modeling projects, coordinating efforts across different departments and sites to achieve outstanding production metrics. Collaborate with site industrial engineering teams to continuously improve production capabilities and optimize capacity. Work closely with ATE and Satellite-Team to plan for future product capacity requirements and evaluate their impact on capital investments. Collaborate with ME, HR, and HVM-Planning teams to bridge the "Plan-To-Performance gap" (P2P gap) by providing comprehensive feedback and thoughtful planning. Capacity Modeling As part of the capacity modeling team, you will be responsible for developing extraordinary models to meet our fab output requirements. Your role will involve: Developing models that accurately reflect fab output capabilities, ensuring they meet the exacting requirements of our production goals. Understanding and facilitating the conversion process for future technology node tr

Who we are About Privy Our mission is to make privacy and user ownership the default online. To do so, we build simple, flexible APIs and tools for developers that make it easy to build new products on crypto rails. Privy owns the abstractions and infrastructure layer above wallets, integrating across chains, third-party providers, and Stripe products like Treasury and Link. We get to solve hard technical problems while leveraging Stripe's distribution to reach customers like Ramp, Klarna, Deel, Kraken, Hyperliquid, and Fomo — powering experiences for both mainstream users and crypto natives. About the team Engineering at Privy is distinguished by: High urgency: Shipping very small iterations, very fast, to learn very quickly. Product taste: Our customers are developers, and to build effective products for them requires technical knowledge - you will often be "the PM". Security mindset: A great portion of our product is trust. While we have a dedicated security team, every engineer brings security to their designs from the start. In practice, we use boring technology like Node, React, and AWS so we can focus our engineering energy entirely on pushing the boundaries of Privy's core product, e.g. through hardware enclaves, multi-region low latency APIs, and blockchain abstractions that are accessible to mainstream developers. What you’ll do As an Engineering Manager at Privy, you will lead, grow, and shape high-performing engineering teams that make a massive impact on our product and industry. Your North Star is to enable and drive towards the success of Privy’s business overall. The buck stops with you: you are the owner of your team’s ultimate impact on Privy’s product and business. We believe that excellence in management is why our team loves working at Privy. Incredible managers are the core driver behind Privy’s long-term success: they are force multipliers, performance coaches, culture-carriers. They build the scaffolding that supports the team and

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! We’re building the next chapter of AI-native craft and creativity—where craft and technology meet. Our unified platform bridges the gap between AI capabilities and human creativity, letting professional creatives and designers harness the power of the world’s leading AI models without losing precision, expression, or soul. Together, we’re building tools that help people move faster without sacrificing quality, precision, or craft; a platform where AI supports creativity instead of flattening it. Figma-Weave designers are highly collaborative self-starters who push their teams to build amazing experiences for all our users. We have a diverse range of experiences and expertise and love learning from each other. Our PMs thrive on solving hard problems, considering every detail, and achieving ambitious goals. We are looking for a Product Designer to join Figma Weave, this role sits at the intersection of generative AI, node-based workflow editing, and creative tools, building a platform that makes sophisticated AI generation accessible through visual workflows. You'll work directly with our founding team to drive critical initiatives - from our workflow editor, to team collaboration features, to strategic integrations with Figma. This is a rare opportunity to join at the ground floor where you'll have outsized impact on product direction and help define how creators interact with cutting-edge generative AI. What you’ll do at Figma Weave: Contribute to overall strategy and decision-making about produ

About the Team The Hardware Health and Observability team owns the end-to-end health lifecycle of OpenAI’s global compute fleet. Our mission is to maximize healthy, usable compute across accelerator vendors, generations, cloud providers, and regions through reliable health signals, automated remediation, and scalable operational tooling. We build the systems that observe, detect, remediate, and verify hardware issues across GPUs, CPUs, networking, and platform infrastructure, enabling frontier model training and inference workloads to run reliably at hyperscale. We are the last line of defense for the success of OAI’s production and research workloads. About the Role On the Hardware Health and Observability team, you’ll build critical infrastructure that keeps OpenAI’s largest compute clusters healthy and operational at scale. Even small numbers of unhealthy systems can impact large-scale training and inference workloads. This team focuses on minimizing downtime, improving fleet efficiency, and ensuring compute resources remain continuously available to researchers and product teams. Engineers on this team own problems end-to-end, from defining health signals and debugging failures to building automated remediation systems that operate across millions of GPUs globally. In this role, you will: Define and maintain health signals across GPUs, CPUs, networking, and platform infrastructure. Build and evolve health checks that detect, remediate, and verify failures at scale. Ensure critical health checks execute with minimal latency to maximize workload uptime. Investigate hardware failures and system-level issues across large-scale compute environments. Own node lifecycle workflows including drain, quarantine, repair, RMA, and return-to-service processes. Build automation and tooling that enables global cluster management with minimal manual intervention. Partner with workload, reliability, and provider teams to integrate health signals into training and inference system

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We run one of the industry's largest AI compute fleets, spanning multiple cloud providers and datacenters, to train, research, and serve frontier AI models. Those fleets run on Kubernetes, and the Kubernetes Platform team owns the control plane that makes them work. We are operating at a scale where the defaults stop working. We own the scheduler and extend it to place topology-sensitive ML workloads across thousands of accelerators at once. We scale the control plane itself — apiserver, etcd, controllers — so it stays responsive as object counts and node counts grow by orders of magnitude. And we build the core cluster services every workload depends on, like service discovery, so they hold up under the same pressure. We make sure the control plane is fast, correct, and always available. Your work will directly determine whether Anthropic can keep reliably and safely training frontier models as our compute footprint continues to grow. Key responsibilities Own, operate, and extend the Kubernetes scheduler for Anthropic's accelerator fleets, including custom scheduling plugins and policies for gang scheduling, topology awareness, and preemption Scale the Kubernetes control plane (apiserver, etcd, controller-manager) to support clusters far beyond typical limits, and find the next bottleneck before it finds us Design, build, and operate core cluster services such as service discovery that every workload in the fleet depends on Build and maintain custom controllers, operators, and CRDs Partner with research, training, and inference to understand workload shapes and turn their requirements into platform capabilities Collabo

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We run one of the industry's largest AI compute fleets, spanning multiple cloud providers and datacenters, to train, research, and serve frontier AI models. Those fleets run on Kubernetes, and the Kubernetes Platform team owns the control plane that makes them work. We are operating at a scale where the defaults stop working. We own the scheduler and extend it to place topology-sensitive ML workloads across thousands of accelerators at once. We scale the control plane itself — apiserver, etcd, controllers — so it stays responsive as object counts and node counts grow by orders of magnitude. And we build the core cluster services every workload depends on, like service discovery, so they hold up under the same pressure. We make sure the control plane is fast, correct, and always available. Your work will directly determine whether Anthropic can keep reliably and safely training frontier models as our compute footprint continues to grow. Key responsibilities Own, operate, and extend the Kubernetes scheduler for Anthropic's accelerator fleets, including custom scheduling plugins and policies for gang scheduling, topology awareness, and preemption Scale the Kubernetes control plane (apiserver, etcd, controller-manager) to support clusters far beyond typical limits, and find the next bottleneck before it finds us Design, build, and operate core cluster services such as service discovery that every workload in the fleet depends on Build and maintain custom controllers, operators, and CRDs Partner with research, training, and inference to understand workload shapes and turn their requirements into platform capabilities Collabo

Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere. The Team The AI Cluster Production Engineering team is part of the AI Compute Platform organization at Biohub, a non-profit research lab committed to open science and open-source AI. We own the design, operation, and reliability of large-scale multi-GPU AI clusters that power frontier AI biology research: protein language models, genomic foundation models, and scientific reasoning systems built to be shared, not monetized. Our clusters run Slurm on Kubernetes infrastructure and support everything from day-to-day AI researcher workflows to multi-node hero training runs at thousands of GPUs. The team works at the intersection of AI tooling, distributed systems, HPC, and frontier AI, debugging deep AI infrastructure problems and building AI systems critical to the entire AI organization. The Opportunity CZ Biohub's mission is to cure or prevent all human disease. Achieving that requires training frontier-scale AI biology models, and that demands reliable, high-performance compute infrastructure. This is production engineering work at a frontier AI lab, with the twist that the mission is biology and the science is open. You'll keep GPU clusters running at high utilization, debug the toughest distributed systems failures, and build the operational foundations for scaling to multi-thousand GPU hero runs. The technical problems are genuinely hard (e.g., multi-node distributed training, InfiniBand fabrics, large-scale storage, Slurm at scale) inside an organization where the work is aimed at helping peop

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As the Engineering Manager, GitLab Delivery - Operate , you’ll guide a globally distributed team focused on making it easier for customers to deploy, upgrade, and run GitLab reliably in their own infrastructure. You’ll help shape the systems and tooling that support environments ranging from single-node virtual machines to large Kubernetes clusters, with a focus on reliability , operational simplicity , upgrade velocity , and zero-downtime capabilities across GitLab.com , GitLab Dedicated , and self-managed deployments. In this role, you’ll partner closely with a Product Manager and work across Infrastruc

Who we are About Privy Our mission is to make privacy and user ownership the default online. To do so, we build simple, flexible APIs and tools for developers that make it easy to build new products on crypto rails. Privy owns the abstractions and infrastructure layer above wallets, integrating across chains, third-party providers, and Stripe products like Treasury and Link. We get to solve hard technical problems while leveraging Stripe's distribution to reach customers like Ramp, Klarna, Deel, Kraken, Hyperliquid, and Fomo — powering experiences for both mainstream users and crypto natives. Learn more about Privy: Privy and Stripe: Bringing crypto to everyone About the team Engineering at Privy is distinguished by: High urgency: Shipping very small iterations, very fast, to learn very quickly. Product taste: Our customers are developers, and to build effective products for them requires technical knowledge - you will often be "the PM". Security mindset: A great portion of our product is trust. While we have a dedicated security team, every engineer brings security to their designs from the start. In practice, we use boring technology like Node, React, and AWS so we can focus our engineering energy entirely on pushing the boundaries of Privy's core product, e.g. through hardware enclaves, multi-region low latency APIs, and blockchain abstractions that are accessible to mainstream developers. What you’ll do Manage and architect multi-tenant infrastructure that serves billions of requests every month Design our build and deploy systems across the stack, from SDKs to apps to high-traffic APIs to complex secure enclaves, and more – all for a team that ships daily Drive observability across our product and help keep our systems up and performant in our role in the most critical parts of our customers' stacks Advocate for making the right technical investments at the right time in a company with ever-evolving technical needs Who you are Minimum requirements 8

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. At Tenstorrent, we build computers for AI, and the developers shaping its future. Our high-performance RISC-V CPUs, modular chiplets, and scalable compute systems give developers full control at every layer of the stack, at any scale from a single-node experimentation to data center-scale deployment. We believe in an open future. Our architecture and software are designed to be edited, forked, and owned. Our team of engineers, dreamers, and first-principle thinkers is redefining how hardware and software converge to accelerate innovation. Enterprises today are searching for sovereign solutions. Help these customers make the leap into next-generation compute. Sitting at the intersection of technical depth and business impact, you’ll guide leaders and architects as they evaluate and adopt new AI platforms. If you thrive in long cycles, complex decision paths, and high-stakes alignment, this is a chance to build an enterprise motion from the ground up and shape how large organizations deploy state-of-the-art compute. This role is remote, based out of New York, NY or Santa Clara, CA. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are Skilled a