Production Tech in United States

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 Production Engineer to join our team. This role is available as a hybrid opportunity 3 days a week in San Jose, CA or Bellevue, WA reporting to Production Engineering in the Cloud Infrastructure & Operations department. Join Zscaler to be a force multiplier for the reliability of a global platform processing 200+ billion transactions daily across tens of millions of enterprise users. In this role, you will provide the technical vision and hands-on execution to drive an "automation-first" culture across the company. By maturing our observability and architectural standards, you will directly reduce our Mean Time to Mitigate (MTTM) and shape the scalability of our globally distributed, multi-cloud infrastructure. What you’ll do (Role Expectations) Implement highly available, scalable infrastructure across AWS, GCP, and bare-metal environments Drive an "automation-first" cult

About the Team The Product & Platform teams at OpenAI are responsible for delivering the company’s most impactful offerings—such as ChatGPT, our API platform, and new enterprise capabilities—to a global and diverse customer base. These systems must perform at scale and deliver exceptional experiences to developers, consumers, and businesses alike. The ChatGPT Multimodal team works across voice, image generation, and other multimodal experiences to turn frontier research capabilities into reliable products. The team connects product usage and failure patterns with research, evaluation, data, inference, capacity, and external partnerships so that model and product improvements translate into better experiences for users. About the Role We are seeking a Technical Program Manager to build the flywheel that helps ChatGPT multimodal products learn from real-world usage and improve quickly. You will lead programs spanning production-signal mining, evaluation and data pipelines, research-to-production parity, multimodal capacity planning, and complex cross-functional dependencies for voice and image-generation launches. You will work closely with product engineering, research, Human Data, inference and capacity teams, safety partners, and external vendors or product partners. Success requires technical depth, strong systems thinking, comfort with ambiguity, and the ability to turn fragmented or manual work into durable mechanisms that teams adopt. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Build a system for mining production conversations and product signals to identify representative multimodal workflows, user needs, and failure modes. Establish and maintain evaluations for the highest-priority multimodal behaviors and use cases, with clear coverage, quality standards, and ownership. Package production signals into decision-ready data and

About the Team OpenAI's data and storage infrastructure spans data platforms, online databases, and file/object storage. These systems underpin data ingestion and processing, durable persistence, indexing and retrieval, and product file experiences. As frontier models and agents evolve how they use memory, history and snapshots, the underlying architecture increasingly shapes the capabilities products can deliver—and their latency, reliability, cost and efficiency. About the Role We are looking for a technically deep TPM to independently define and lead multiple programs across data platforms, online databases and storage infrastructure. You will connect model, product and data-consumer requirements to architecture, and work with the relevant engineering teams to take new capabilities through production adoption and repeatable expansion. The design scope is exabyte-scale storage and infrastructure spanning multiple millions of CPU cores. The challenge is not simply forecasting more resources: it is making complete, workload-ready capacity repeatable, with a clear path from product requirements through architecture, deployment and validation. A data pipeline, database query, file operation or execution snapshot can affect whether a product or agent succeeds; you will connect those outcomes to the systems underneath. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Translate model, product and data-platform needs into precise access patterns, consistency, durability, freshness, availability and scalability requirements. Connect memory, history, retrieval and resumable work to capability and end-to-end latency. Partner with engineering to transform data and storage architecture into repeatable scale units: standardized provisioning, placement, routing, data movement and readiness checks that bring storage, compute and networking online together.

About Team Our Robotics team is focused on unlocking general-purpose robotics and advancing toward AGI-level intelligence in dynamic, real-world environments. Working across the full model and systems stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the physical constraints of real-world systems to improve people’s lives. About the Role We are looking for a Technical Program Manager to own actuator development and integration from system goals through production readiness. The actuator program spans mechanical, electrical, firmware, harnessing, controls, test, reliability, manufacturing, and supply chain, and needs a TPM who can turn cross-functional decisions into clear scope, executable milestones, and timely decisions. In this role, you will help the team converge on the right technical plan, surface risks early, and deliver reliable actuator systems on schedule. This role is based in San Francisco, CA and requires in-person presence 4 days a week. In this role you will: Drive actuator programs end-to-end, aligning scope, milestones, interfaces, dependencies, and exit criteria across engineering teams. Drive scope lock and technical convergence for sprints, MVPs, and stretch goals while connecting component decisions to system performance. Coordinate actuator development across motors, gears, sensing, electronics, and firmware, and align the electrical and mechanical interfaces that connect actuators to the broader robot. Lead validation planning from early prototypes through engineering validation, reliability testing, and production readiness. Drive tradeoff decisions across cost, quality, performance, schedule, and lead time by collaborating cross-functionally and quantifying impact to meet program deliverables. Establish effective mechanisms for technical reviews, change control, design releases, decision tracking, and manufacturing readiness.

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . The Platforms TPM organization partners with Experimentation, ML, LLM/GenAI, and Data Infrastructure teams to shape how Pinterest measures everything that matters — from product experiments to AI systems to the platforms everyone builds on. What you'll do: As a Staff Technical Program Manager for Measurement, you'll have the rare opportunity to shape how a company the size of Pinterest measures itself — turning a portfolio spanning experimentation, machine learning, generative AI, and data infrastructure into one coherent, high-impact program. Drive Pinterest's experimentation roadmap — accelerating how confidently and quickly teams can test, learn, and ship new ideas at scale. Own the program driving cost and compute efficiency across our ML systems, and help scale data science workflows into production-grade tooling. Lead cost optimization and

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:

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 a Growth Engineer to own the technical foundation of Modal's marketing and developer-facing web surfaces: the marketing site, docs site, growth landing pages, high-profile microsites, forms, analytics instrumentation, and the integrations that help users discover, understand, and get started with Modal. This is a frontend-heavy role for someone with strong product taste, web engineering craft, and a business-owner mindset. You'll partner with Product Engineering, Design, Data, and Growth to ship polished, measurable web experiences from high-profile projects like the GPU Glossary and LLM Engine Advisor to internal tooling that helps teams publish content faster. When this role is going well, Modal launches new pages, docs experiences, campaigns, and experiments quickly without sacrificing performance, craft, or measurement. In this role you will:

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: 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 spik

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

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE At Baseten, we’re looking for a Technical Program Manager to drive our most complex, cross-cutting infrastructure programs. This role will operate across all domains of AI infrastructure, from the GPUs up to the multi-cluster orchestration layer. This is an execution-first role. The work is less about owning a single system and more about imposing order on ambiguity: standing up the right structures, driving decisions to closure, and making sure nothing falls through the cracks across dozens of stakeholders. If you take satisfaction in turning a chaotic, half-defined initiative into a predictable, well-governed program, this role is for you. RESPONSIBILITIES Own complex migrations end to end. Lead large-scale infrastructure migrations across teams and domains. This will involve scoping the work, sequencing dependencies, managing risk, and driving them to completion without surprises. Drive process across infrastructure. Establish and run the operating rhythms that keep programs healthy: planning cadences, status reporting, decision logs, risk reviews, and escalation paths. Make the process light enough that teams adopt it and rigorous enough that it actually works. Help managers build the right structures. Partner with engineering managers and leads to design the team structures, ownership boundaries, and working models a program needs to succeed. Spot gaps in accountability before they become problems. Own fo

We are hiring a Senior Technical Product Marketing Manager to lead positioning and messaging and to grow adoption of MongoDB Search and Vector Search as foundational components of our platform – the retrieval layer powering the next generation of grounded AI applications and agents. This is a high-impact role for a marketer who thinks like a builder. As developers architect increasingly sophisticated systems – RAG pipelines, agentic workflows, multi-modal search experiences – retrieval has moved from an implementation detail to a core design decision. You’ll join a high-performing, globally distributed team and partner closely with Marketing, Builder Relations, Product Management, Engineering, Partners, and Sales to develop, measure, and achieve cross-functional goals. The role requires technical depth in information retrieval — lexical and vector search, hybrid approaches, embeddings, re-ranking, agentic retrieval loops, and the tradeoffs that matter in production systems — paired with the product marketing instincts to turn that depth into crisp, differentiated messaging for distinct user and buyer personas. Hands-on experience building or shipping AI-enabled products is a strong advantage. Individuals with prior experience in technical sales, developer relations, or technical marketing are encouraged to apply. This person is a voracious consumer of AI research and pays close attention to shifting patterns in application architectures and development, including agentic systems. This individual is confident in communicating with technical practitioners and non-technical decision makers in one-to-few and one-to-many engagements for internal and external audiences. We are looking to speak to candidates who are based in the US for our hybrid working model. What You’ll Do Drive Strategy & Execution: Act as a strategic partner for high-impact initiatives that align with MongoDB’s long-term business goals in collaboration with Marketing, Developer Relations, Product

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role As an AI Accelerator Systems Software Technical Program manager at OpenAI, you will help bring our chips/system hardware roadmap to life, navigating an array of technical and partnership challenges. We’re looking for people excited to push the frontiers of computing by navigating technical explorations and are passionate about building. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Manage the end-to-end software development from design to implementation for our AI acceleration systems, working across technical, cross-functional and external stakeholders Lead planning and scheduling of AI system software designs with our strategic partners and vendors Coordinate and lead internal resources and communication for efficient interaction with partners and vendors. You might thrive in this role if you: Have experience as a software technical program manager for data center system products (server, GPU, TPU, networking, storage and so on) taking products from concept to volume in a data center environment ensuring the systems scale with high quality Know end-to-end software development program management techniques from concept, design, production, deployment into the data center Want to help design some of the world’s largest supercomputing systems, working at the edge of complex hardware challenges Enjoy working with and enabling world-clas