Distributed Systems Engineer in New York

About Datadog: We're on a mission to build the best platform in the world for engineers to understand and scale their systems, applications, and teams. We operate at high scale—trillions of data points per day—providing always-on alerting, metrics visualization, logs, and application tracing for tens of thousands of companies. Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way. The Opportunity: Datadog’s Senior Staff Engineers are technical leaders operating at the forefront of large-scale systems design, building the infrastructure that will support our next five years of growth and beyond. They do this in three major ways: As individual contributors, they bring world-class technical depth to build industry-leading systems in areas such as observability data platforms, distributed query engines, and real-time event streaming at global scale. As technical leaders, they apply broad architectural perspective and deep systems thinking to align design decisions across teams and domains. They work across complex, multi-team problem spaces to define long-term technical direction, drive large-scale initiatives forward, and ensure consistent execution. As engineering stewards, they play a key role in evolving our systems and engineering culture. They actively participate in Datadog’s senior technical community, bringing external insights and internal experience to elevate engineering standards and mentor the next generation of technical leaders. Examples of projects a Senior Staff Engineer may lead include designing and launching a new distributed data storage engine capable of handling hundreds of millions of records per second, building the real-time infrastructure behind a new observability product, or re-architecting a core service to support exponential growth in throughput and complexity. What You’ll Do: Be the technical owner of multiple critical systems or architecture areas, often spanning several t

Role Overview You’re a seasoned Site Reliability Engineer who loves owning complex infrastructure, making things run faster, safer, and with less manual effort. In this Staff‑level role, you’ll design and operate VMware‑based private cloud platforms that power mission‑critical SaaS products used by customers around the world. You’ll work across Linux, Windows Server, networking, storage, and automation frameworks to increase reliability, reduce toil, and modernize a global datacenter environment. You’ll have the scope to set technical direction, build automation at scale, and mentor engineers while staying hands‑on with VMware vSphere, F5/AVI load balancers, and hybrid Active Directory. Here’s a breakdown of what you’ll do (not all of it, just the important stuff) Lead the architecture, deployment, and ongoing optimization of VMware vSphere–based private cloud infrastructure across multiple global datacenters. Design and build automation using PowerShell/PowerCLI, Ansible, Python, and CI/CD tools to streamline provisioning, configuration, and compliance. Administer, harden, and troubleshoot Linux (RHEL/CentOS/Ubuntu) and Windows Server environments that host enterprise and SaaS workloads. Integrate and manage Active Directory for authentication, access control, and service accounts across hybrid on‑prem and cloud environments. Partner with network and security teams to manage firewalls, VPNs, storage, and load balancers (F5 BIG‑IP, AVI/NSX Advanced Load Balancer) for highly available services. Document architectures and runbooks, participate in on‑call and change management, and mentor engineers while influencing long‑term reliability and automation strategy. These are the essentials you’ll need to get an interview 10+ years of experience in systems or infrastructure engineering, including operating large‑scale enterprise or SaaS datacenter environments. Deep hands‑on expertise with VMware vSphere (ESXi, vCenter, DRS, HA, vMotion, distributed switches) in production

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 an Infrastructure Security Engineer to design and secure the core systems that power our platform. This role focuses on building security directly into our infrastructure—from container isolation and orchestration to identity and secrets management in a multi-tenant, cloud-native environment. You’ll work closely with engineering teams to define secure primitives and ensure our platform is resilient, scalable, and trustworthy by design. This is a hands-on, deeply technical role focused on real systems, not compliance or policy. What You'll Do: Platform & Runtime Security Design and improve isolation mechanisms for multi-tenant workloads (containers, sandboxing, execution environments) Strengthen boundaries between customers, workloads, and internal systems Identify and mitigate risks in distributed, dynamic compute environments Container &

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 role is hybrid requiring 2 days in office at our San Francisco or NYC hub every Tuesday & Wednesday. About the Role Machine Learning is a cornerstone at Taskrabbit, and we're looking for a Staff Machine Learning Engineer to join our team and lead the next phase of our customer retention strategy. This is a critical, full-stack role for an individual who is passionate about the end-to-end lifecycle: from initial research and model development to building the robust systems that power repeat customer engagement and lifetime value growth at scale. Taskrabbit's greatest growth opportunity lies in deepening customer relationships and accelerating repeat purchases. Our most valuable customers are those who return frequently, discover new service categories, and increase their spending over time. There's significant untapped potential in the marketplace: repeat customers spend 3-5x more than one-time users, and category expan

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 role is hybrid requiring 2 days in office at our San Francisco or NYC hub every Tuesday & Wednesday. About the Role Machine Learning is a cornerstone at Taskrabbit, and we're looking for a Staff Machine Learning Engineer to join our team and lead the next phase of our customer retention strategy. This is a critical, full-stack role for an individual who is passionate about the end-to-end lifecycle: from initial research and model development to building the robust systems that power repeat customer engagement and lifetime value growth at scale. Taskrabbit's greatest growth opportunity lies in deepening customer relationships and accelerating repeat purchases. Our most valuable customers are those who return frequently, discover new service categories, and increase their spending over time. There's significant untapped potential in the marketplace: repeat customers spend 3-5x more than one-time users, and category expan

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. With a billion rides per year and counting, Lyft is solving hard problems in a rapidly growing domain with a lot of data and creative solutions in Rider, Driver, Marketplace, and beyond. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building a next-generation platform for low-cost, ultra-immersive transportation to improve people's lives warrants modern ML utilizing petabyte-scale data. Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business. The Fulfillment group, within the Marketplace at Lyft, is responsible for determining what inventory can be reliably offered for a given rider session and fulfilling rider requests. The group comprises several sub-teams that generate feasible offers for riders, match rider requests with drivers, and maintain a distributed state machine to track rides and drivers from request through completion. We are seeking a Machine Learning Engineer to join the Fulfillment team and lead the design, development, and deployment of state-of-the-art machine learning systems. This role requires a strategic thinker who can balance high-level system architecture with hands-on technical implementation. You will collaborate across teams to shape the future of ride-sharing by leveraging machine learning and data science. Responsibilities: Design, build, and deploy machine learning models for real-time applications, including translating state-of-the-art research into production-ready solutions Design and implement feature pipelines, model training workflows, and serving infrastructure using Lyft's ML platform Evaluate ML system performance against business KPIs, run experiments, and drive continuous model improvement

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 Storage Services team operates Pinterest's scalable online structured data storage platform—supporting both SQL-based table models and complex graph data structures—managing 100TB+ datasets and serving over 1.5M queries per second across many of Pinterest's most important products. We're looking for an exceptional Staff Software Engineer to lead the technical strategy and execution of our storage infrastructure initiatives, defining how these systems are designed, built, and operated. You'll drive innovation across distributed SQL, high-throughput/low-latency query processing, graph workloads, and the developer experience for storage clients. What you’ll do: Provide technical guidance and direction to a high-performing team building reliable, performant, and cost-efficient storage systems that operate at massive scale and power busines

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? We're building the data infrastructure behind some of the most demanding AI training workloads in the world, and we want sharp, curious people to help us do it. In this role, you'll build and maintain the high-performance data layer our Modeling teams rely on for training and evaluation jobs. As a Software Engineer, Data Infrastructure, you will: Work directly on petabyte-scale storage infrastructure, and the networking and performance challenges that come with it. Collaborate daily with researchers and engineers who are some of the best in the world at what they do. You may be a good fit if you have: 4+ years of experience working on data storage infrastructure Strong command of Python Kubernetes experience, especially on the storage side (Persistent Volumes, CSI drivers, etc.) The ability to transform unstructured data into performant datasets across diverse storage backends including S3, GCS, and POSIX Experience with distributed data processing frameworks such as Apache Beam, Spark, or Flink [Nice-to-have] Familiarity with modern analytics tooling such as BigQuery, Airflow, or dbt Genuine excitement about AI.

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? We're building the foundational infrastructure that will define how the world thinks about and deploys AI, and we want the sharpest, most curious people to help us do it. As a member of our Analytics & Data Insights team, you'll tackle the kind of problems that don't have textbook answers yet, launch products that didn't exist a year ago, and help enterprises understand what foundational AI actually means for their bottom line. As a Data Engineer, you will: Work directly on new customer experiences built on one of the most advanced AI systems in the world Collaborate daily with researchers and engineers who are some of the best in the world at what they do Run implementations end-to-end and see initiatives through to real outcomes Partner across research, marketing, sales, and finance to help define how Cohere grows, with your recommendations feeding directly into products and strategy You may be a good fit if you have: 5+ years of experience working on production-grade data processing systems Strong command of Python and SQL Experience with distributed data processing frameworks such as Apache Beam, Spark, or

Datadog's Application Performance Monitoring (APM) provides deep visibility into the health, performance, and lifecycle of modern distributed applications, tracing requests from end-user devices (web and mobile) through to backend services. Our goal is to help customers detect root causes faster, optimize application performance, and improve resource efficiency at scale. As the Engineering Manager for APM Serverless, you will help define and deliver the end-to-end serverless APM experience, from auto-instrumentation through troubleshooting, and ensure that OpenTelemetry and Datadog-native customers alike have a frictionless and performant journey. You will also lead efforts to expand coverage of cloud-managed services across providers, ensuring customers can seamlessly trace and monitor critical services in all major and emerging cloud environments. We’re looking for an experienced engineering leader who thrives at the intersection of infrastructure and developer experience. You should care about well-designed APIs, observability-first thinking, and building systems that empower other developers. This is a high-leverage role that will influence how developers across the industry understand and instrument their serverless workloads. 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: Lead a polyglot team of 8-9 engineers and partner closely with Product and Engineering teams across Datadog to deliver industry-leading serverless capabilities that power consistent, scalable, and intuitive instrumentation across languages. Drive a domain that is technically rich: Lambda, Azure Functions, GCP, OTel billing, Rust, durable functions, distributed tracing across managed services. Engineers on this team work

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-

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

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? Our team is a fast-growing group of committed researchers and engineers. The mission of the team is to build reliable machine learning systems and optimize audio inference serving efficiency using innovative techniques. As an engineer on this team, you will work on advancing core audio model serving metrics, including latency, throughput, and quality by diving deep into our systems, identifying bottlenecks, and delivering creative solutions for audio processing and streaming workloads. You’ll collaborate closely with both the training and serving infrastructure teams to ensure seamless integration between model development and deployment, with a special focus on real-time and streaming audio inference. Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations. You may

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? Large Language Models (LLMs) continue to push the boundaries of what AI systems can do — but inference is still the bottleneck. The Model Efficiency team is responsible for pushing the limits of LLM inference efficiency across our foundation models. We explore and ship breakthroughs across the model execution stack, including: model architecture and MoE routing optimization decoding and inference-time algorithm improvements software/hardware co-design for GPU acceleration performance optimization without compromising model quality Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations. As a Staff Research Engineer, you will develop, prototype, and deploy techniques that materially improve how fast and efficiently our models run in production. You may be a good fit

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? Our team is a fast-growing group of researchers and engineers focused on building reliable ML systems and pushing the boundaries of LLM inference efficiency. We develop techniques that improve how models execute in production, driving lower latency, higher throughput, and consistent quality across diverse workloads. As an engineer on this team, you’ll work across the inference stack to improve core performance metrics by diving deep into model execution, identifying bottlenecks, and developing innovative optimizations. You’ll collaborate closely with modeling and systems teams to experiment, measure, and ship improvements that meaningfully accelerate inference. As the team evolves, you’ll have opportunities to build expertise in advanced performance techniques, including GPU/CUDA optimizations, kernel-level improvements, and model execution strategies for MoE and large-scale architectures. Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, e