Production Cleaning Specialist in United States

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. With Roblox Ads business growing at a rapid rate, we are building large scale ads machine learning infrastructure to deliver effective performance ads to our users, and more business values to our advertisers. We’re looking for an EM to lead a team of exceptional ML infrastructure engineers, build scalable, reliable, and high-performance infrastructure that powers ML systems across our organization. You’ll operate at the scales of hundreds of billions of engagements, and redefine how we deliver performance ads to hundreds of millions of users. You Will: Lead strategic planning and roadmap execution of scalable production-ready ML systems including model training, data pipelines, feature engineering and model inference. Own the architecture, establish engineering best practices of scalability, reliability, and cost-effectiveness of ML infrastructure (e.g., training, serving, feature). Work closely with data scientists, ML engineers, platform teams, and product stakeholders to design, implement, and operate robust ML platforms that accelerate model development and deployment. Recruit, mentor, and grow a high-performing team of ML infrastructure engineers. You Have: 5+ years of experienc

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As a Principal Security Software Engineer on the Production IAM team, you will set the technical direction for how identity and access work across Roblox's production infrastructure, from the mTLS-based identity that services use to authenticate to one another, to the privileged access controls that govern how engineers reach production. The team is accountable for Roblox's machine and workload identity platform, its centralized authorization engine, its production access management platform, production PKI and certificate lifecycle, and just-in-time privileged access for engineers. As an individual contributor in Production IAM, you will define multi-year strategy, drive alignment across Roblox Platform, mentor senior and staff engineers, and personally build the hardest parts of these systems. As AI agents become first-class actors in production, you will also help pioneer how they get identity, prove who they are, and receive safely-scoped access. You will Lead the architecture for production identity and access. Define and evolve the end-to-end design for machine, workload, human, and AI-agent identity across our hybrid on-prem and cloud fleet, making secure access invisible when

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. With Roblox Ads & Discovery business growing at a rapid rate, we are building large scale ads machine learning infrastructure to deliver more value to our users and our advertisers. As a Machine Learning Infrastructure Engineer, you’ll build scalable, reliable, and high-performance infrastructure that powers ML systems across our organization. You’ll operate at the scales of hundreds of billions of engagements, and redefine how we deliver performance ads to hundreds of millions of users. You will: You will co-design models and systems, working at the intersection of model architecture and ML infrastructure, partnering closely with core modelers, data and AI infrastructure engineers, and product teams to push the boundaries of large-scale training and serving. Your work will span recommendation, search, and agentic applications, including large transformer architectures, LLMs, generative rankers, and efficient offline and online content-understanding systems. You will investigate model, data, and systems tradeoffs end to end—from data pipelines and distributed training to low-latency inference and production serving. This includes designing efficient KV-cache strategies, applying p

Applied AI is where Datadog's ambitious AI bets get built and shipped ( Bits Chat , updog ). We sit at the intersection of research and product: turning promising capabilities from Datadog AI Research lab and the research community into production systems that reach real customers. The team builds the foundations for agentic systems capable of operating at scale in complex production environments. Current bets span agents that run autonomously at scale, context and memory layers that make those agents more intelligent over time, and tools that help customers build and validate AI-native services in production. The mandate is to move fast from idea to customer impact, and when a product finds its footing, to set it up for growth. As an Engineering Manager I in Applied AI, you will lead a team of engineers and applied scientists working on one of these challenges. You will define technical direction, run short feedback loops, make deliberate decisions about what to pursue or stop, and work closely with product managers, research teams, and cross-functional partners to ship AI capabilities that matter. At Datadog, we place value in our office culture, the relationships and collaboration it builds and the creativity it brings. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do Lead and develop a team of engineers and applied scientists focused on building the foundations for agents operating at scale Work closely with product managers, research teams, and cross-functional partners to shape the team's bets from initial framing through to broader adoption, with a clear definition of success criteria at each stage Own end-to-end delivery of high-quality AI systems, from early research exploration to production-grade reliability, with high standards for operational excellence, system reliability, and technical quality Navigate the unique challenges of shipping AI-powered products: balancing quali

The ML Observability team builds cutting-edge tools to monitor, explain, and improve AI systems in production, particularly those leveraging Large Language Models (LLMs) and generative AI. We provide robust, scalable observability for AI workloads, including drift detection and model evaluation, and behavior tracing, enabling customers to ship AI with confidence. As a Staff Engineer, you’ll lead the development of new features and foundational capabilities within Datadog’s LLM Observability product. You will shape product direction, drive experimentation, and apply your deep understanding of both AI systems and software engineering to solve open-ended problems in the fast-moving AI landscape. Your work will directly impact how our customers monitor, troubleshoot, and optimize LLM-based applications in production. Join us in building the foundational tools that make AI systems observable, understandable, and reliable in the real world. 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: Drive design and implementation of LLM observability features. Ideate, prototype, and scale new product features to provide insights and drive improvements for generative AI systems Work cross-functionally with other eng teams, product, UX, and applied science to iterate fast and find product-market fit Develop and extend tools for tracing, evaluating, and debugging LLMs Influence architecture decisions and mentor engineers to build resilient, high-performance systems Stay close to customer pain points and use those insights to guide product and engineering priorities Stay current with industry trends and advancements in machine learning and observability, driving innovation within the team Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering or r

Datadog’s Cloud Networks team designs, builds, and maintains the production network infrastructure that powers everything built on top of our platform across AWS, GCP, Azure, and beyond. In this role, you’ll set technical direction for how we scale our multi-region, multi-cloud network footprint while keeping reliability and performance high. You’ll partner closely with internal teams and Cloud Service Providers to troubleshoot complex connectivity issues, integrate new networking capabilities, and improve the foundations our engineers and customers rely on. This is a high-impact opportunity to drive meaningful improvements in scale, resiliency, and cost efficiency. 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: Design, build, and operate cloud network infrastructure across AWS, GCP, Azure, and Neoclouds in a multi-region environment. Own connectivity between clouds, customers, and developers—ensuring scalable, secure, and reliable network paths. Set clear technical direction for expanding data centers and evolving the network while maintaining stability and performance. Improve cross-site and cross-region connectivity patterns to support Datadog’s growing platform needs. Lead deep investigations into latency, packet loss, and connectivity failures – from pcap and path analysis through to escalations with cloud providers that may originate from customer support Identify and deliver network-related efficiency and cost-saving opportunities that positively impact business health. Who You Are: You have deep networking expertise. You understand BGP, route policies, path selection, prefix advertisement, and what breaks in large-scale networking. You have substantial experience designing, building, and evolving large-scale Software-Defined Networks—inclu

Datadog’s People Technology team is building the future of AI at work. We’re looking for a People Systems Developer to design and deploy AI-native workflows that transform how we hire, develop, and support our employees. This is a high-impact hands-on builder role. You’ll move beyond traditional HRIS configuration to prototype and productionize AI-enabled systems across talent acquisition, onboarding, performance, workforce planning, and internal service delivery. You’ll operate with high autonomy, partner directly with stakeholders, and ship solutions that measurably improve how our People team works. 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 Design, build, test, and implement AI-enabled workflows and tools, moving from proofs of concept to full production and adoption based on user feedback. Rapidly prototype using LLMs, APIs, and automation frameworks — and move validated ideas into secure, scalable production systems. Integrate AI capabilities into our ecosystem (Workday, Greenhouse, Slack, Snowflake, Jira, Google Workspace, and more). Partner directly with stakeholders like People Analytics, HRBPs, IT, Legal, and Security to ensure solutions are usable, compliant, and follow responsible AI practices. Evaluate AI tools and vendors through hands-on experimentation. Enable the broader People team to adopt AI effectively and responsibly. Success in this role includes reducing manual People workflows by at least 20% through AI-enabled automation and intelligent system design. 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. Who You Are 3-5 years of exper

Datadog is seeking a Director of Product Management to lead our AI Observability portfolio and shape how organizations build, monitor, and scale AI systems in production. This role leads LLM Observability and helps define the next wave of innovation across GPU Monitoring, Distributed AI Monitoring, and emerging research-oriented tooling such as Model Lab. You will set the vision and strategy for this rapidly growing area, expanding established products while incubating new capabilities that deliver deep visibility into AI infrastructure, model performance, and distributed AI environments. As AI becomes core to modern applications, this team plays a critical role in ensuring customers can deploy and scale AI with confidence. We’re looking for a builder-minded product leader with strong technical depth and hands-on curiosity - someone who has built or worked closely with AI-powered products and understands the realities of production AI. You will lead a team of product managers and partner closely with engineering and design to advance Datadog’s leadership in AI observability. 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: Own the vision and strategy for AI-driven products, ensuring alignment with overall company goals and customer needs. This will include managing our embed program to enhance the capabilities of existing products as well as developing dedicated and independent AI products. Lead and mentor a team of product managers, helping them grow and advance their careers while ensuring the delivery of high-quality, AI-powered features. Collaborate with cross-functional teams including engineering, data science, marketing, and sales to deliver AI product solutions that meet customer needs and business objectives. Identify new opportunities for

Senior Forward Deployed Engineers (FDE) sit at the intersection of enterprise customer environments and a fast-moving internal product. They partner directly with customers to design, build, troubleshoot, and improve production solutions, and they are ultimately accountable for helping customers get to production. This role is best suited for engineers who want to own technical outcomes end to end: understanding what a customer is trying to build, shipping the integration, and feeding learnings back into the product roadmap. This role will be based remotely in the United States (East Coast). Responsibilities Customer success Serve as the primary technical owner for customer engagements from initial discovery through production rollout Understand each customer's architecture, constraints, and definition of success, and drive toward that outcome Manage expectations, communicate risks clearly, and help customers navigate technical decisions with confidence Technical integration Build the connectors, pipelines, and supporting tooling needed to make the platform work inside real enterprise environments Write production-quality code, troubleshoot issues, and implement fixes directly in active workstreams Work effectively within customer environments that have different stacks, infrastructure, and integration constraints Product feedback loop Capture product feedback with precision, including logs, reproduction steps, and a clear proposed path forward Use customer engagements to identify product gaps, surface recurring patterns, and help improve the product roadmap Document technical decisions and tradeoffs clearly so product and engineering teams can extend the work Workstream collaboration Partner closely with product and engineering teams to help turn field patterns into reusable product capabilities Contribute directly in focused workstreams by helping drive technical design, implementation, and delivery Make sound engine

About Taskrabbit: Taskrabbit is a marketplace platform that conveniently connects people with Taskers to handle everyday home to-do’s, such as furniture assembly, handyman work, moving help, and much more. At Taskrabbit, we want to transform lives one task at a time. As a company we celebrate innovation, inclusion and hard work. Our culture is collaborative, pragmatic, and fast-paced. We’re looking for talented, entrepreneurially minded and data-driven people who also have a passion for helping people do what they love. Together with IKEA, we’re creating more opportunities for people to earn a consistent, meaningful income on their own terms by building lasting relationships with clients in communities around the world. Taskrabbit is a hybrid company with employees distributed across the US and EU and a Built In — Best Places to Work (2022, 2023, 2024, 2025) continually ranked across multiple national and regional categories. Join us at Taskrabbit, where your work will be meaningful, your ideas valued, and your potential unleashed! This is a hybrid role that will require two days in-office each week on Tuesdays and Wednesdays at our SF location on 130 Sutter Street. About the Role Every business function at Taskrabbit — Marketing, Customer Support, Finance, Operations — is a "customer" with real workflows, real data, and real friction. Your job is to embed with them, scope their use cases, and build the Claude-powered agent, automation, or tool that solves them. This is an internal-facing role — there is no external customer or product work. Reporting to the Director of AI Strategy and Enablement, you'll operate the way an FDE operates at a high-growth AI company: full ownership of a deployment from discovery through production and direct accountability for whether what you ship actually changes a metric. In most cases you'll own a build end-to-end solo; in some functions you may partner with that team's own subject-matter expert to pair domain depth with

About the Team The Ecosystem AI Deployment Engineering (ADE) team supports strategic partners as they build high-quality technical integrations into ChatGPT and Codex. Our goal is to create products users depend on, drive adoption and retention, and build an ecosystem where partners win when OpenAI wins. About the Role We are looking for an AI Deployment Engineer to help strategic partners design, build, evaluate, submit, launch, and maintain high-utility plugins for ChatGPT and Codex. This is a hands-on, partner-facing product engineering role for someone who can contribute to the platform itself, lead sophisticated partner engagements, and translate ambiguous product needs into production-ready integrations. You will work across partner product and engineering teams and OpenAI's product, engineering, partnerships, legal, policy, design, and go-to-market teams. You will identify the right use cases, prototype and review implementations, run evaluations, debug issues across systems, guide partners through submission and review, and support launch and post-launch iteration. The best person for this role moves fluidly between code, product judgment, project leadership, and clear communication with engineers and executives. This role is a fit for a product minded engineer who wants to stay close to users and partners while still going deep on code, reliability, evaluations, and developer experience. The goal is to help partners ship plugins that are not merely technically functional, but genuinely useful in ChatGPT and Codex. This role is based in our San Francisco office. 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: Own the technical partner journey for priority B2B plugins—from pitch and readiness assessment through architecture, build, evaluation, submission, launch, and ongoing maintenance. Identify strong plugin use cases, define crisp user journeys and expected behaviors, and

About the Team OpenAI’s Infrastructure Operations team is responsible for the availability, reliability, and operational excellence of one of the world’s largest AI infrastructure networks. The team owns day-to-day operations of production AI networks across Industrial Compute's data centers, working with colocation providers, deployment teams, and hardware vendors to deliver highly available GPU infrastructure for AI training and inference workloads. About the Role We are seeking an Infrastructure Operations Engineer to operate and improve the large-scale Ethernet fabrics that support GPU clusters, storage systems, and management infrastructure. This role combines hands-on production operations with automation, observability, and incident response across a global AI network. The ideal candidate has experience operating high-availability data center, cloud, AI, or HPC networks and can move comfortably from physical-layer troubleshooting to routing and fabric behavior, change execution, and root-cause analysis. You will partner closely with network architecture, systems engineering, GPU engineering, storage engineering, security, deployment, site operations, service providers, colocation partners, and hardware vendors to raise reliability and reduce operational toil. Key Responsibilities Own the operational health, availability, and reliability of production AI network infrastructure across Industrial Compute's data centers. Monitor, troubleshoot, and resolve network incidents while meeting service-level objectives (SLOs), reducing Mean Time to Detect (MTTD), and minimizing Mean Time to Recovery (MTTR). Operate and maintain large-scale Ethernet fabrics supporting GPU compute, storage, and management networks. Execute production network changes, maintenance windows, and capacity expansions with minimal customer impact. Manage the hardware lifecycle, including switch and optics replacements, RMA coordination, software upgrades, and preventive maintenance. Support new A

About the Team Data Platform at OpenAI owns the foundational data stack powering critical product, research, and analytics workflows. We operate some of the largest Spark compute fleets in production; design, and build data lakes and metadata systems on Iceberg and Delta with a vision toward exabyte-scale architecture; run high throughput streaming platforms on Kafka and Flink; provide orchestration with Airflow; and support ML feature engineering tooling such as Chronon. Our mission is to deliver reliable, secure, and efficient data access at scale and accelerate intelligent, AI assisted data workflows. Join us to build and operate these core platforms that underpin OpenAI products, research, and analytics. We’re not just scaling infrastructure – we’re redefining how people interact with data. Our vision includes intelligent interfaces and AI-assisted workflows that make working with data faster, more reliable, and more intuitive. About the Role This role focuses on building and operating data infrastructure that supports massive compute fleets and storage systems, designed for high performance and scalability. You’ll help design, build, and operate the next generation of data infrastructure at OpenAI. You will scale and harden big data compute and storage platforms, build and support high-throughput streaming systems, build and operate low latency data ingestions, enable secure and governed data access for ML and analytics, and design for reliability and performance at extreme scale. You will take full lifecycle ownership: architecture, implementation, production operations, and on-call participation. You’ve supported Spark, Kafka, Flink, Airflow, Trino, or Iceberg as platforms. You’re well-versed in infrastructure tooling like Terraform, experienced in debugging large-scale distributed systems, and excited about solving data infrastructure problems in the AI space. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per wee

About the Team OpenAI’s Applied AI Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. We work with customer executives, product and engineeriIng teams, security leaders, and transformation teams to identify valuable opportunities, accelerate technical implementation, and scale what works. Enterprise deployments are defined by complexity rather than any one industry: existing architectures, diverse data environments, security and governance requirements, multiple stakeholder groups, and organization-wide change. We turn lessons from these deployments into better products and reusable patterns for customers everywhere. About the Role As an Enterprise Applied AI Engineer you will partner directly with leading organizations to design, build, and deploy AI systems that deliver measurable business outcomes. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from use-case selection and architecture through prototyping, evaluation, production launch, and scale. You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions involving model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact—not simply activity or successful demonstrations. This is a rare opportunity to work on consequential real-world deployments at the frontier of AI while directly influencing how OpenAI’s products evolve. This role is based in our SF or NYC office. 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: Partner directly with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteri

About the Team The Storage Infrastructure team builds and operates the storage foundation behind OpenAI’s most demanding workloads. We work directly with research to design storage systems for rapidly evolving experiments, while also powering production at scale. We own the platform end to end: backend systems, user-facing services and APIs, and the control planes that manage how data is placed, moved, and retained over time. Our stack spans cloud and in-house object stores across very different workload profiles, from GPU-attached systems to dedicated storage hardware. We also build the federation layer that unifies these backends behind a simple interface and routes each workload to the right storage solution. About the Role You will help build the storage platform that powers OpenAI’s research and production systems. This is a hands-on infrastructure role for engineers who want to work on deeply technical systems at scale and own them in production. You’ll work across object storage, cross-region data movement, lifecycle management, and the federation layer that provides a unified interface across multiple backends. Much of our stack runs on Kubernetes, and we primarily build services in Rust. In this role, you will: Build and operate storage services that underpin OpenAI’s research infrastructure Develop object storage systems across cloud and in-house environments Build systems for cross-region data movement, replication, and recovery Design lifecycle management capabilities that keep data durable, available, and cost-effective Evolve the federation layer that unifies multiple backend systems behind a simple interface Improve performance, reliability, and operational excellence across the platform Collaborate closely with researchers and infrastructure teams to support rapidly evolving workloads You might thrive in this role if you: Have experience building or operating distributed systems in production Have worked on storage infrastructure, object stores, dist