Staff Platform Manager in United States

Datadog is entering a new chapter in how our product looks, feels and operates. We're building a small, high-leverage Design Lab team to define that evolution, and we're looking for a Senior Staff Visual Designer to author it. Our design system powers everything we ship. Now we're ready to evolve it. As Datadog expands into AI-driven experiences and more complex product surfaces, the visual language needs to grow with it. This is the role that decides what that language is — someone with taste, judgment, and the confidence to set a direction and defend it. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do Define and evolve Datadog’s visual language across product surfaces. Lead exploratory and concept work, creating a range of distinct visual directions for the next generation of the product. Synthesize product context, research, references, and stakeholder input into clear visual principles and a coherent point of view. Establish the quality bar for typography, color, composition, iconography, imagery, motion, and visual expression across the platform. Create high-fidelity product exemplars and prototypes that make an emerging direction tangible. Partner deeply with Brand, Product Design, Motion, and Design Systems to test and refine the direction across different surfaces. Influence executives and senior stakeholders through clear, persuasive visual storytelling. Guide and critique the work of other designers, helping the visual direction remain coherent as it evolves. Help shape how visual exploration, critique, and craft are integrated into Datadog’s design process. You will report directly to the Design Director and work as part of a small, focused team defining the future state before it scales across hundreds of designers and engineers. Who 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 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

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 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

Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange™️ platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 160+ public exchanges globally and thousands of private exchanges at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform. We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler. Role We are looking for a Sr. Staff Software Development Engineer-AI Security to join our team. This is a Hybrid (based in San Jose, CA or Bellevue, WA with a 3 days in office requirement) role, reporting to the Director of Software Engineering in the Emerging Tech department. You will be responsible for designing and implementing core infrastructure components and distributed systems, serving as a foundational architect for our AI security solution. This high-impact role focuses on scaling security infrastructure to support hundreds of millions of users, collaborating with stakeholders across the development lifecycle to drive innovation and technical excellence. What you’ll do (Role Expectations) Architect, develop, and optimize a low-latency, high-throughput AI Security plane utilizing Rust, specifically leveraging its async/await model for highly efficient I/O and service-oriented architecture Build resi

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're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work. What you'll do: We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This role is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustnes

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? Are you energized by leading the design of high-performance, scalable and reliable machine learning systems? Do you want to set technical direction and help shape the next generation of AI platforms powering advanced NLP applications? We are looking for a Lead Member of Technical Staff to join the Model Serving team at Cohere. The team is responsible for developing, deploying, and operating the AI platform delivering Cohere's large language models through easy to use API endpoints. In this role, you will provide technical leadership across multiple teams, driving the architecture and strategy for deploying optimized NLP models to production in low latency, high throughput, and high availability environments. You will serve as a key point of contact for customers, leading the design of customized deployments to meet their specific needs, and mentoring engineers to raise the technical bar across the team. You may be a good fit if you have: 8+ years of engineering experience running production infrastructure at a large scale, with a track record of technical leadership Demonstrated experience leading the architecture

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. We are looking for a talented engineer to join our CPU design team and lead the front-end RTL physical implementation team. Drive CAD flows on multiple process technologies while working closely with core micro-architects to refine CPU core configurations and optimizing PPA. You’ll work on a CPU based on RISC-V ISA, collaborating with DV, PD, RTL and performance teams to deliver a functional, timing, and power-converged design. This role is hybrid, based out of Austin, TX 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 An expert in physical design practices used to optimize PPA Experienced in high-performance physical design. Proficient in RTL coding (Verilog/VHDL) and familiar with industry-standard tools for simulation and power analysis. Skilled in synthesis, place and route tools including flows and physical design methodology. Background in CPU micro-architecture. What We Need Own front‑end physical implementation and PPA definition for a high‑performance RISC‑V CPU and CPU subsystem Work closely with microarchitects and RTL designers to “make the IP better” by optimizing frequency, power, and area

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. We are looking for a Sr. Staff Design Verification Engineer to lead end-to-end verification efforts for our advanced CPU and AI compute platforms. This role is ideal for seasoned engineers with deep expertise in CPU verification who excel at driving complex test plans to closure, mentoring teams, and leveraging modern AI tools to accelerate innovation. This role is hybrid, based out of Boston, Toronto, Ottawa or Santa Clara. 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 Proven track record leading end-to-end CPU verification across processor cores, caches, interconnects, interrupts, MMU/IOMMU, and cache coherency protocols, with multi-chip and emulation experience a plus. Expert in creating comprehensive test plans and building simulation testbenches using SV-UVM, C/DPI, and Cocotb. Skilled in managing high-volume regression execution, complex failure triage, and driving test plans and coverage metrics to closure. Experienced leader and mentor dedicated to guiding junior engineers and fostering verification best practices across teams. Proficient in leveraging modern AI tools like Copilot, Cursor, Claude, Gemini, and ChatGPT to optimize ver

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. Curious about how cutting-edge hardware actually comes to life? We're looking for someone who’s excited to dive into the core of next-gen systems and help make them real. In this role, you’ll validate high-speed interfaces, solve complex system-level puzzles, and collaborate across teams to shape the future of AI/ML computing. If firmware, hardware, and hands-on debugging sound like your kind of fun — let’s chat! This role is hybrid and based in Vancouver, Canada. 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 You love bringing hardware to life and enjoy the thrill of solving tricky problems across the hardware–firmware boundary. You’re hands-on in the lab and comfortable with tools like oscilloscopes, protocol analyzers, and JTAG — digging deep doesn’t scare you. You’re comfortable jumping into unfamiliar problems and figuring things out — whether it’s in the lab or in firmware. You’re curious, collaborative, and excited to work on technology that pushes the boundaries of performance. What We Need Someone to take the lead in validating our next-gen PCIe interfaces — from controller to PHY, and everything in between. A strong co

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. We are looking for a Design Verification Engineer to contribute to the unit-level verification of our RISC-V CPU front end—including instruction fetch, branch prediction, and surrounding fetch control structures. As a key individual contributor within our front-end DV team, you will focus on building testbenches, generating stimulus, and developing checkers for complex microarchitectural scenarios. This role is hybrid, based out of Austin, TX 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 Front-End Experience: Solid background verifying CPU front-end blocks—such as instruction fetch, branch predictors (BTB, TAGE, RAS), instruction caches/TLBs, or decode—using SystemVerilog, UVM, and C++. Unit-Level Focus: Hands-on experience building clean, controllable unit testbench environments with precise stimulus and checking. Reusable Design: Pragmatic approach to building transactors, predictors, and scoreboards that can be reused across verification levels. Collaborative Problem Solver: Comfortable working alongside RTL designers to trace and fix misspeculation, redirect, and edge-case fetch bugs. What We Need Unit-Level Verifica

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 a Backend Engineer on the Chat Engine team, you'll build the engine behind GitLab Duo Chat, the conversational AI experience for GitLab. You'll work primarily in Python to build and maintain our agentic runtime: the Flow Registry, LangGraph flows, and the Duo Workflow Service. You'll also work in the GitLab Rails monolith, where Chat connects with the product. You'll own scoped parts of the system, ship small features and improvements with minimal guidance, and collaborate with the team on larger projects. You'll work alongside senior and staff engineers who will partner with you on design and support

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. The AI Studio is the team responsible for designing, building, and operationalizing internal software platforms across every functional department of the company, including People Operations, Customer Support, Sales, Marketing, Finance, Recruiting, and Executive Operations. We treat each internal department as its own product surface, and in truth as its own startup, with its own customers, its own metrics, and its own reasons for existing. We ship custom software, built on Replit’s own platform, to solve operational problems that off-the-shelf SaaS tools cannot serve well at our scale and pace of growth. Position Summary The Member of Technical Staff (AI Builder) is a full-time builder role with substantial autonomy. You will design, architect, and ship AI-powered internal platforms that run the operations of the company across many departments at once. This is not a role for someone who wants a narrow, well-defined lane. It is a role for someone who wants to walk into an ambiguous business problem, understand it well enough to argue about it with the department lead, and then ship the software that fixes it. We are looking for people who genuinely understand the mechanics of a business: how revenue is actually made, why recruiting velocity matters, what makes support scale or break, how finance closes a month, and why each department is critical to whether the company wins or loses. You do not need to have run every function, but you need to respect why each one exists and be able to reason about it like an operator, not just an engineer. Because we treat each department as its own startup, we strongly favor people who have either run their own business or worked on a fast-scaling startup. You know what it feels like

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