Staff Engineer Fullstack in United States

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 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 collecti

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 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 strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Requirements: 5+ years of experience writing high-quality production code Experience building high-performance distributed systems at a large scale (the more battle scars, the better) Strong cloud skills Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.) Experience with performance engineering (tell us a story of when you shaved off a few milliseconds!) Ability to work in-person in our NYC or SF office. Prior experience with Rust is nice to have, but not required. Ability to participate in on-call rotation and respond to production incidents.

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 strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models towards higher throughput and lower latency, we’d love to hear from you! Requirements: 5+ years of experience writing high-quality, high-performance code. Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT). Familiarity with Nvidia GPU architecture and CUDA. Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc). Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).

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're looking for strong backend engineers who love building a developer tools used by the largest AI companies in the world. You’ll be building for things at scale, but also for new AI workflows that change every day. Requirements: Experience building and shipping modern web applications end-to-end. We care more about what you’ve built than how many years you’ve been building. Comfort working across the stack: TypeScript on the frontend, Python services on the backend, and ClickHouse for data and analytics. Deep knowledge of observability tools and patterns used for large-scale workloads such as custom sandboxes, training and inference for large language (LLM) and diffusion models. Experience with at least one of: billing/payments systems, B2B SaaS tooling, or enterprise software, or LLM / diffusion models inference and training loads. Strong product instincts; yo

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’re looking for strong engineers with experience building developer tools that users love to work with. Our ideal candidate is someone with a demonstrated drive to build beautiful interfaces that enhance developer productivity. Requirements: 5+ years of experience developing high-quality Python libraries with broad user-bases, ideally including some experience maintaining open-source software. Knowledge of advanced Python features, especially async programming. A strong product sense that manifests as a focus on developer ergonomics and productivity. A high level of customer empathy, good communication skills, and an openness to working directly with our users to help solve their problems. Ability to participate in on-call rotation and respond to production incidents. Ability to work in-person in our NYC or Stockholm office. Any of the following would be a plus:

This Engineering Manager will lead the Data Visualizations Explorations team within the Graphing organization, setting product direction, and coaching and developing team members. They will staff and drive projects that build end to end experiences for Datadog’s core users: observability engineers. This includes extending the capabilities of core widgets like Hostmap and Geomap Visualizations and finding innovative ways to leverage existing Datadog data sources. This role also involves close partnerships with other product teams to deeply understand customer needs and deliver compelling data experiences. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Work with Product Management and Design to plan and staff projects for the Data Visualization Explorations team Coach and develop engineers at various levels Ensure strong cross-team communication and design best practices around project management, code review, architecture patterns, and more. Proactively anticipate cross-team dependencies and blockers to goals. Identify opportunities to appropriately reuse or customize features across dashboards, notebooks, and product pages. Deeply understand the needs of our customers and other Datadog products we work with. Participate in customer conversations, review product briefs, read feature requests, and coach team members to adopt these practices as well. Define and maintain high standards for operations practices, including bug triage and remediation, incident response, and gathering and analyzing performance telemetry for our widgets. Who You Are: At least 2 years of people management experience in a software engineering or similar setting Strong TypeScript/JavaScript skills, including familiarity with front

Who we’re looking for and why now? A great writer who gets technical stuff – and loves figuring out how to explain it to other people. We have a strong writing culture, and writing as marketing has worked ridiculously well for us. We have a newsletter with over 100k subscribers, active social accounts, and real SEO and AEO authority. Hundreds of thousands of people visit our website every day. If you join, you won't spend a year building an audience before anyone reads you. We can and like to be opinionated about what we think best practice is and thousands of developers look to us to provide them opinion. The problem is we're at capacity, and there's a long list of content we're neglecting because of it. We used to do more tutorials. Use case guides people keep asking us for. Overall, we have a lot to write about. PostHog has grown from product analytics into a broad platform for engineers, and we're building everything from AI agents and error tracking to data infrastructure at huge scale. We're also open by default, so we're unusually happy to share what we've learned along the way. We want someone who can turn all of that into genuinely useful technical content. Simply put, you're a writer . You care about the craft, have opinions about what makes technical content actually great, and have a portfolio that proves it. What you’ll be doing You'll research and explain use cases, technical concepts, and how things actually work. One day that might mean digging into how our engineers rebuilt part of our data warehouse. Another could be explaining the difference between logs and traces, writing a tutorial for a PostHog use case, or figuring out the best way to explain what a context warehouse is. You'll need to be comfortable getting into technical details, but you don't need to be an engineer. The important bit is that you're curious enough to understand technical concepts properly and a good enough writer to explain them simply. Day to day, it looks like: Writing co

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Senior Manager, Platform Engineering / DevOps Who Are You You are an experienced Senior Manager / emerging Staff-level leader in DevOps and Platform Engineering with strong technical depth and demonstrated leadership in delivering enterprise-scale cloud platforms. You bring a balanced mix of hands-on engineering expertise, team leadership, and execution rigor. You excel in driving outcomes in complex, multi-stakeholder environments, guiding teams to deliver secure, scalable, and high-quality platform solutions. You are comfortable leading engineers, managing stakeholders, and owning delivery across multiple workstreams. You demonstrate: A strong ownership mindset with accountability for delivery and outcomes Ability to translate business needs into actionable engineering roadmaps Solid expertise in cloud-native platforms, DevOps practices, and SRE principles Capability to lead teams and influence without requiring extensive tenure Role Responsibilities Development & Enforcement Own and execute the H100 platform engineering roadmap, aligned to enterprise priorities and program milestones Drive delivery of GCP-based platform capabilities (GKE, networking, IAM, CI/CD, observability) Establish and enforce engineering standards, best practices, and ADR compliance <li

We anticipate the application window for this opening will close on - 14 Oct 2026 Careers that change lives start here. Medtronic is a global leader in healthcare technology with a Mission to alleviate pain, restore health, and extend life. Our 95,000 employees work across more than 150 countries to put patients first — developing innovative medical technologies that improve the lives of 72&#43; million patients each year. Your unique talents will help shape the future of healthcare while building a career grounded in purpose, growth, and impact. A Day in the Life The Principal Field Service Technical Training Specialist role is to train field service engineers (FSE), hospital biomed staff, and others on Medtronic cranial and spine medical device repair and maintenance. While this position is posted as remote, the facility where training takes place is in Lafayette, CO. and is required to be at the facility full time a minimum of 2 weeks per month to conduct instructor-led training. Medtronic will not cover travel costs to be on site, so only candidates willing to be located close to the facility will be considered. The Principal Field Service Technical Training Specialist has the responsibility and authority to drive, create and deliver training for global field service employees, and hospital Bio-Med customers . This individual will be primarily responsible for creating and delivering the course curriculum for Cranial and Spinal products and technologies, with a focus on X-Ray and Imaging products to educate both employees and customers on the technical knowledge required to troubleshoot, repair, and maintain products in these businesses. Key Responsibilities: Instructional Design & Project Management Collaborate

Lead Analyst Company: The Boeing Company Boeing F-22 Mission Systems team is hiring for a Lead Analyst located in Berkeley, MO . As Lead Analyst, you will be responsible for analyzing engineering solutions for the F-22 Raptor, including both enhancing existing field units with the addition of new capabilities as well as architecting and testing completely new systems. In this role you will be joining a cross-functional team to progress our products through the development lifecycle. Position Responsibilities: Leads analysis and translation of complex requirements into system architecture, hardware and software designs and interface specifications. Must be able to work in a fast-paced, collaborative environment, engaging with multiple teams and being responsive to agile requirements and critical path customer need dates. Collaborates with Responsible Engineers, customer technical staff, suppliers, and Lockheed Martin teammates to develop and document complex electronic and electrical system requirements for F-22 avionics sub-systems and weapon system architecture Analyzes and translates requirements into system architecture, hardware and software designs and interface specifications in support of lab and operational environments Reviews test data, including off-nominal data, for accuracy, quality and/or fidelity prior to delivery to customer This position is expected to be 100% onsite. The selected candidate will be required to work onsite at one of the listed location options. This position requires the ability to obtain a US Secret Security Clearance for which the US Government requires US Citizenship A final Secret Clearance

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Senior Manager, Platform Engineering / DevOps Who Are You You are an experienced Senior Manager / emerging Staff-level leader in DevOps and Platform Engineering with strong technical depth and demonstrated leadership in delivering enterprise-scale cloud platforms. You bring a balanced mix of hands-on engineering expertise, team leadership, and execution rigor. You excel in driving outcomes in complex, multi-stakeholder environments, guiding teams to deliver secure, scalable, and high-quality platform solutions. You are comfortable leading engineers, managing stakeholders, and owning delivery across multiple workstreams. You demonstrate: A strong ownership mindset with accountability for delivery and outcomes Ability to translate business needs into actionable engineering roadmaps Solid expertise in cloud-native platforms, DevOps practices, and SRE principles Capability to lead teams and influence without requiring extensive tenure Role Responsibilities Development & Enforcement Own and execute the H100 platform engineering roadmap, aligned to enterprise priorities and program milestones Drive delivery of GCP-based platform capabilities (GKE, networking, IAM, CI/CD, observability) Establish and enforce engineering standards, best practices, and ADR compliance</li

Manager I, Engineering - Onboarding Growth This Engineering Manager will lead the Onboarding team within the Shared Capabilities organization, setting product direction, and coaching and developing team members. They will staff and drive projects that drive new users to adopt Datadog in their organizations. From the first time a new user signs up for a Datadog trial, this team is responsible for making sure they have a world class setup and onboarding experience, and can realize the full value of Datadog in their organization. Onboarding owns the “front door” of Datadog, the very first experience new users and prospective customers encounter. This work is extremely important to company success - improving our onboarding process directly impacts new users ability to find value with Datadog quickly. This role also involves close partnerships with other product teams to deeply understand customer needs and deliver compelling first-time user experiences At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Work with Product Management and Design to plan and staff projects for the Onboarding Growth team Coach and develop engineers at various levels Ensure strong cross-team communication and design best practices around project management, code review, architecture patterns, and more Build UX features to streamline the onboarding and trial experience through rapid experimentation Build out data platforms and use that data to personalize the onboarding experience for each user Work directly with product teams to ensure each product has a best in class new user experience Create AI-assisted setup options to make sure initial setup “just works” out of the box Champion the use of analytics data to analyze user behaviour and apply those insights

About the Team The Statsig team at OpenAI builds and operates the experimentation platform that powers product development, measurement, and decision-making across the company. We partner closely with product, engineering, and infrastructure teams to ensure experiments are trustworthy, statistically rigorous, and scalable to the needs of frontier AI products. Our mission is to help teams make better decisions through reliable experimentation. We care deeply about statistical correctness, pragmatic solutions, and building systems that researchers and engineers can trust at massive scale. The team operates at the intersection of experimentation methodology, data infrastructure, causal inference, and product analytics. We are looking for experienced experimentation experts who want to shape the future of experimentation in the AI era. About the Role We are hiring a Staff-level Data Scientist to help lead the evolution of OpenAI’s core experimentation platform. This role is focused on improving the statistical rigor, reliability, and practical usability of experimentation across the company. You’ll work on some of the hardest problems in online experimentation: sample ratio mismatch detection, variance reduction, bias mitigation, metric design, triggered analysis, heterogeneous treatment effects, sequential testing, and experimentation in complex ML systems. You’ll also help translate advanced statistical concepts into pragmatic systems and product experiences that teams can actually use. This is a highly technical individual contributor role with significant influence across methodology, platform architecture, and experimentation best practices. The ideal candidate combines deep statistical expertise with strong systems intuition and hands-on experience building or operating experimentation platforms at scale. In this role, you will: Drive the statistical direction and technical strategy for OpenAI’s experimentation platform Design and improve experimentation methodolo