Ai Infrastructure System Engineer Bangalore in New York

Why join us Brex is the intelligent finance platform that enables companies to spend smarter and move faster in more than 200 markets. By combining global corporate cards and banking with intuitive spend management, bill pay, and travel software, Brex enables founders and finance teams to accelerate operations, gain real-time visibility, and control spend effortlessly. Brex’s AI-native automation and world-class service eliminate manual expense and accounting tasks for customers so they can focus on what matters most. Tens of thousands of the world's best companies run on Brex, including DoorDash, Coinbase, Robinhood, Zoom, Plaid, Reddit, and SeatGeek. Working at Brex allows you to push your limits, challenge the status quo, and collaborate with some of the brightest minds in the industry. We’re committed to building a diverse team and inclusive culture and believe your potential should only be limited by how big you can dream. We make this a reality by empowering you with the tools, resources, and support you need to grow your career. Engineering at Brex Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders. What you’ll do As a Senior Software Engineer, Infrastructure (Release Engineering) at Brex, you will design, build, and operate the core systems that power Brex’s release, observability, and incident management processes. You will partner closely with product, platform, and operations teams to ensure releases are safe, fast, and reliable, and that our infrastructure scales securely as Brex grows. Where you’ll work This role will be based in our New York office. We are a hybrid environment that combines the energy and connections

As a Senior Platform Product Manager focused on AI SDLC Trusted Throughput, you will define and drive the product strategy for enabling safe, reliable software delivery at AI-native scale across Datadog’s Internal Developer Platform. As AI accelerates development velocity and system complexity, you will help evolve SDLC systems from human-supervised workflows to platforms with built-in safety, observability, and correctness guarantees. You will partner closely with engineering, security, and developer platform teams to improve deployment reliability, operational visibility, and governance while enabling both engineers and AI agents to move quickly with confidence. This role offers the opportunity to shape foundational developer infrastructure and influence how AI-powered software delivery operates across Datadog. 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 product strategy, roadmap, and execution for AI-native SDLC throughput and reliability initiatives across Datadog’s Internal Developer Platform Define and drive platform outcomes aligned to DORA metrics, balancing deployment velocity with reliability, change failure reduction, and operational safety Partner with engineering, infrastructure, security, and developer experience teams to build automated validation, auditability, and risk-scoring capabilities into deployment workflows Deliver actionable SDLC observability and diagnostic capabilities that connect executive-level metrics to operational signals across the software delivery lifecycle Drive systems that monitor and validate AI-generated or AI-attributed changes to ensure correctness, compliance, and trustworthy automation Serve as a cross-functional product leader across SDLC Foundations, Security Engineering, and compl

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).

Datadog is seeking curious, driven interns to join our Product Management team and help build products that improve how engineers monitor and understand their systems. As a Product Management Intern, you'll support the product development lifecycle by partnering closely with Engineering, Design, and Product Marketing to bring new ideas and features to life. You'll gain hands-on experience working on products that serve highly technical customers while contributing to meaningful business and user outcomes. Interns are embedded directly within product teams, working on meaningful initiatives alongside full-time Product Managers and contributing to actual product decisions. Our platform processes over 100 trillion events per day across 30,000+ customers in a multi-cloud environment -- giving you direct exposure to large-scale, real-time systems built by engineers, for engineers. It's an environment where you'll develop product thinking, technical communication, and cross-functional collaboration skills by doing the work, not just observing 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: Conduct customer discovery conversations and gather feedback to better understand user needs Drive product initiatives from concept through launch alongside Engineering, Design, and Product Marketing teams Translate customer and business needs into clear product requirements and engineering priorities Analyze customer feedback, product data, and market insights to help inform product decisions Prepare and deliver technical product demonstrations and communication materials Develop technical understanding of Datadog’s observability platform and cloud infrastructure products Who You Are: Targeting a 2028 full-time graduation or start date Pursuing a degree in

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 Engineering Manager to lead a team of highly experienced engineers building the infrastructure that powers Modal's serverless GPU platform. This is a hands-on leadership role — expect to split your time between technical contribution and people management depending on what the team needs. You'll set direction, remove blockers, and build a strong engineering culture as your team tackles hard problems in distributed computing, large-scale data handling, and performance optimization. Who You Are You're an experienced engineering leader who stays close to the work and builds alongside your team when it counts. You earn trust through technical depth, not title. You communicate clearly, help strong engineers move fast without cutting corners, and stay calm and pragmatic under pressure. You care as much about how your team gets to an answer as the answ

Coordination Systems provides foundational distributed systems building blocks for internal Datadog platforms. Our services cover sharding, consensus, resource protection, configuration distribution, and much more. We are looking for a manager to lead the Coordination Systems - Storage team. This team provides essential configuration storage and distribution systems that are depended upon by almost every service and pod at Datadog. We power critical runtime configuration (e.g. feature flags), complex control planes (e.g. dynamic sharding configuration), and much more. Storage is one of four subteams within Coordination Systems. If successful, the candidate will have opportunities to lead other growing and impactful areas such as Resource Protection. 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: (Describe role responsibilities here/max 6 bullets) Lead a core team of 5 engineers (distributed, with majority in NYC) Lead ceremonies, prioritize and delegate project Stay hands-on with the code, e.g. isolated features, small remediations, investigation follow ups Stay actively involved in operations, incidents, root cause analysis, etc. Constantly promote a culture of operational excellence, organizing gamedays, conducting operational reviews, staying proactive with reliability Who You Are: (Describe role qualifications here/max 6 bullets) Strong distributed systems skills, able to understand and account for a variety of failure modes, well-versed in end-to-end o11y, validation testing, simulation setup, etc. Worked on platform teams before, providing critical infrastructure to internal stakeholders Experienced in handling significant incidents, both as a responder and follow-up ow

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

The opportunity Datadog’s Infrastructure products help engineers understand and operate the systems their applications depend on. Our customers work in complex environments like Kubernetes and serverless, where infrastructure changes constantly, information is dense, and decisions about reliability, performance, and cost are closely connected. We’re looking for a Staff Product Designer to join Modern Compute, with an initial focus on Containers Autoscaling. Autoscaling helps engineering teams make better decisions about how their applications and infrastructure use resources. Designing these experiences requires making deeply technical systems understandable, helping customers act with confidence, and fitting into the tools and workflows they already use. The team is rethinking how workload and cluster autoscaling come together as a more coherent product experience. This includes how customers get started, understand recommendations, evaluate value, and safely apply changes across their environments. The work also connects to other parts of Datadog, including observability, Cloud Cost Management, permissions, and AI-assisted workflows. As a Staff Product Designer, you will help define that direction and lead the work from early problem framing through shipped product. You will partner closely with product and engineering, bring a high level of interaction and visual craft to complex workflows, and help raise the quality of design across Modern Compute. 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 help our Datadogs find a work-life rhythm that works for them. What you’ll do Lead end-to-end product design for Modern Compute, initially focused on our Autoscaling product. Help define the product direction for an area that is still evolving, from early framing and exploration through detailed design and delivery. Design clear, trustwort

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

At 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, enabling seamless collaboration and problem-solving among Dev, Ops, and Security teams globally for tens of thousands of companies. Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way. The Observability Data Platform (ODP) is the backbone of everything Datadog delivers – powering how data is ingested, stored, routed, and surfaced across every product at planet scale. As a Senior Product Manager for ODP, you will work with world-class engineers and cross-functional partners to shape how the platform is deployed, controlled, and operated. You will define product direction across the control plane and data layer, translate complex infrastructure trade-offs into clear roadmap decisions, and help customers get the most from their observability investment – regardless of architecture, topology, or scale. 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 Will Do: Develop a deep understanding of the Observability Data Platform customers – platform engineers, SREs, and product managers that own the product verticals – their infrastructure challenges, deployment topologies, and cost-to-serve trade-offs. Define product direction across multiple ODP surfaces, including the control plane and data layer, by articulating clear problem statements and desired outcomes, and partnering with engineering on technical approach and sequencing Lead conversations with design partners and strategic customers to understand real-world platform pain points, validate product assumptions, and guide solutions from early prototypes through General Availability Develop a co

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 AI Research — Scholars Program with Carnegie Mellon University Datadog AI Research (DAIR) is partnering with Carnegie Mellon University to support a small number of PhD students working on open research problems grounded by ongoing efforts at Datadog/DAIR. You will frame a problem, run your own experiments, and write up what you find, with compute and data at a scale most academic labs cannot provide. You will collaborate with colleagues working on the same questions. The Lab And The Research: DAIR is an industrial research lab motivated by practical challenges in observability and software operation: detecting and diagnosing failures, understanding complex production environments, and helping engineers operate software more effectively. The lab focuses on creating specialized foundation models, post-training and evaluating AI agents, and building frontier-scale machine learning systems. By combining fundamental research with Datadog's large-scale, real-world data and infrastructure, the lab develops new AI capabilities and translates them into practical systems with meaningful impact. Internship projects are shaped with your DAIR mentor and your CMU faculty advisor. You do not need prior experience with observability, monitoring, or infrastructure. What You'll Do: Own a research project end to end: framing the question, running the experiments, writing it up Work directly with a DAIR mentor engaged in the same problem, and stay connected to your advisor and lab Publish, and use the work toward your dissertation See research reach production, when it works Who You Are: Currently enrolled in a PhD program at Carnegie Mellon in machine learning, computer science, statistics, or a related field Depth in at least one area relevant to the research above Comfort running real experiments — training models, working with GPUs, reading and reimplementing recent papers Evidence you can do research: conference or workshop papers, preprin

The Detection Platform organization is responsible for helping customers identify, understand, and act on issues across their environments through alerting, event intelligence, and autonomous detection capabilities. As Director, Detection Platform, you will lead a group of engineering managers and teams responsible for foundational alerting infrastructure, event management, monitor creation experiences, and AI-powered detection systems. This role sits at the center of Datadog’s efforts to evolve how customers detect, investigate, and respond to operational issues at massive scale. You will partner closely with Product Management, Applied Science, Design, and Engineering leaders to shape the future of detection and observability experiences for Datadog customers while leading a growing organization of engineers. 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: Lead a multi-team engineering organization responsible for alerting, event management, monitor creation experiences, and autonomous detection capabilities. Define and execute the technical and organizational strategy for the Detection Platform while aligning stakeholders across Engineering, Product, Design, and Applied Science. Drive innovation in AI-powered detection, anomaly identification, and signal generation that helps customers proactively identify and resolve issues. Scale highly available platform systems that process hundreds of millions of evaluations while maintaining reliability, performance, and operational excellence. Develop and mentor engineering managers and technical leaders, fostering a culture of execution, collaboration, and technical rigor. Champion customer-centric product thinking by balancing platform investments with intuitive user experiences and measurable customer

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 We're looking for a Marketing Operations Manager who can own and harden the systems layer of Baseten's Marketing engine. Marketing at Baseten is scaling fast — more spend, more campaigns, more model launches, more inbound. The systems underneath (Our ESP, CRMs, forms, tracking, routing, alerting) need an owner who treats them like production infrastructure that cannot go down. When something breaks, it costs us time, pipeline, and trust in the data. This role exists so it doesn't break. You’ll simultaneously build for the future and re-think assumptions about our tech stack in the age of agents. This is an offensive play that gives the rest of the team leverage and superpowers to hit our ambitious goals. This is NOT an IT or service role. This is a core member of the marketing team who implements technology to achieve outcomes. RESPONSIBILTIES Own the marketing tech stack end-to-end: ad platforms, email systems, tracking, pixels, forms, connectors. Build defense-in-depth on inbound: spam/bot protection, rate limiting, email/domain validation, sync gating — and the alerting to catch anomalies before they hit sales or leadership dashboards. Enforce data integrity: UTM governance, campaign membership, lifecycle stages, lead scoring and routing logic, field-level hygiene, canonical metric definitions. Operationalize the web request pipeline with our dev agency: structured briefs, tickets, SLAs, and launch-day runb