Ai Infrastructure Systems Engineer Jobs

NVIDIA is building the software foundation for the next generation of AI infrastructure. As AI workloads continue to evolve, Kubernetes and the cloud-native ecosystem must evolve with them. Our team’s mission is to make Kubernetes the best platform for AI workloads. We work upstream with the Kubernetes and CNCF communities to design new APIs, build production-grade implementations, and shape the future of cloud-native infrastructure for AI. As a Senior Software Engineer, you'll tackle the most challenging problems at the intersection of Kubernetes, distributed systems, and AI infrastructure. What you'll be doing Design, implement, and upstream new capabilities for Kubernetes and CNCF projects. Collaborate with engineering teams across NVIDIA to identify AI infrastructure challenges and solve them through upstream innovation. Work closely with Kubernetes SIGs, Working Groups, and the broader open-source community to design and implement new capabilities. Participate in architecture discussions, API design, technical proposals, and code reviews. Build reliable, scalable infrastructure software for next-generation AI workloads. Write clear technical documentation and design proposals. What we need to see B.Sc/M.Sc or higher in Computer Science, Computer Engineering, or a related field or equivalent practical experience. 5+ years of software engineering experience building distributed systems, cloud infrastructure, or platform software. Deep understanding of Kubernetes and the cloud-native ecosystem. Experience extending, building, or contributing to Kuberne

AI Software Engineer, Agent Harness Location: Bengaluru, Karnataka (or throughout India remote-friendly with travel) About EnCharge AI EnCharge AI is building the next generation AI platform. Our novel in-memory-computing architecture delivers a 10x step-function improvement in compute energy efficiency and performance for AI inference workloads. As the demands of artificial intelligence move beyond today's models, we believe fundamental underlying infrastructure must evolve. We are an experienced team of AI researchers, silicon & systems engineers, and architects backed by leading investors, poised to become the essential platform for the next wave of AI innovation. The Opportunity We serve open-weight models and our own bespoke checkpoints on EnCharge hardware. The models change often, and the harness around them needs to keep up. You own this layer that runs agents against files, tools, documents with permissions, memory, unattended execution, and real outputs. It will be assembled from a combination of open-source and bespoke code. Key Responsibilities Own the harness architecture end to end — agent loop, safe execution, context management, knowledge base, memory, permissions, orchestration, outputs, interfaces, observability — one component per layer, with clear interfaces so layers can be swapped. Build the pieces with no open-source equivalent e.g. session semantics, enforced permissions, memory in a human-editable file, orchestrator, and outputs. Keep pace with the models: adapters, prompt formats, tool-call schemas, stop conditions, benchmarking and evaluation. Make tool use reliable across models of uneven tool-calling quality — validation, repair, retries, fallbacks. Develop agents, tools, and MCP servers for internal and customer use cases, and review them for security before they ship. Build the evaluation harness: task suites, regression runs on every model or harness change, cost and latency per task alongside quality. Define the interfaces:

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role The Applied AI team at Ramp is at the forefront of leveraging AI to drive innovation across our platform. We are seeking strong full-stack engineers who are proficient in web frameworks, backend development, and infrastructure. You will work on exciting projects such as AI Agents, Retrieval-Augmented Generation, Structured Extraction (we made https://github.com/1rgs/jsonformer ), internal tooling for customer-facing teams, fine-tuning models, and build infrastructure for LLM inference. If you're passionate about working on real production use cases of large language models (LLMs) and want to contribute to groundbreaking AI applications, this role is for you. What You’ll Do Ship full-stack AI projects end to end Build and integrate components for AI infrastructure, supporting production-level inference and fine-tuning Develop and improve engineering processes, tools, and systems to scale AI solutions across Ramp Create tools and internal platforms to enhance the productivity and capabilities of Ramp's AI and engineering teams What You

About the Job Set the performance direction behind large-scale AI systems built for real deployment. As a Principal AI Performance Engineer, you will define how Graphcore analyzes and optimizes AI training and inference workloads. Your work will shape decisions across hardware, software, networking and system architecture. You will lead the hardest performance investigations, from model behavior to multi-node scaling. You will turn profiling, benchmarking and modeling evidence into technical roadmaps and engineering priorities. You will set measurement standards, guide distributed communication strategy and review critical C++ and Python tools. Your judgment will help teams make better decisions when data is incomplete. This role gives you rare influence across AI infrastructure at data center scale. It is based in Austin, Texas. The team and culture The System Engineering Performance team architects, evaluates and optimizes high-performance infrastructure for large-scale data center deployments. The team works across the computing stack to understand real system behavior. Work happens through evidence, technical ownership and direct cross-team influence. You will guide strategy, resolve complex tradeoffs and raise performance-engineering standards across the organization. Decisions are shaped by benchmarks, simulations, models and clear technical debate. The team values engineers who think big, act fast, take responsibility, speak up and lead beyond their own area. What we’re looking for · Deep experience profiling and optimizing AI, machine learning or high-performance computing workloads at scale. · Proven technical leadership across complex performance-engineering or system-architecture initiatives. · Expert C++ and Python skills, including reliable tools or performance-sensitive software. · Expert understanding of compute, memory, communication and scaling behavior in distributed systems. · Ability to define benchmarks, measurement practices or optimization sta

RDQ427R351 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Search plays a foundational role in this mission. Whether through keyword-based retrieval, semantic similarity via vector embeddings, or hybrid approaches that combine both, our Search technologies help customers find, discover, and understand information across massive, complex datasets. These capabilities power everything from Retrieval Augmented Generation (RAG), AI assistants, and recommendation systems to enterprise knowledge management, in-product search, and data exploration. As a Staff Engineer for Search, you’ll play a critical role in building and scaling our next-generation Search product — spanning Vector Search, Keyword Search, and Search Quality. You’ll drive the design and evolution of a highly-performant, cost-efficient, and developer-friendly Search stack that enables customers to build AI applications with accuracy, speed, and scale. Beyond hands-on contributions, you’ll help define the long-term vision, mentor senior engineers, collaborate across teams, and lead strategic efforts that expand the technical and business impact of Search at Databricks. Check out our recent blog post: Decoupled by Design: Billion-Scale AI Search The impact you will have: Drive the architecture and evolution of the Search product, delivering scalable, low-latency, and high-quality retrieval across massive multimodal datasets. Identify and solve challenges in indexing, storage, query execution, and ranking — blending innovations in vector search, keyword search, and hybrid retrieval techniques. Partner with infrastructure, product, and research teams to shape APIs and developer experiences that

The AI platform is responsible for all AI infrastructure across Datadog. Our mission is to provide tools and platforms that enable data scientists and engineers to conduct large-scale training and inference with ease. We support products such as Bits AI , LLMObs and all our AI research . As an engineering manager for the Training & Serving team, you’ll join a new and fast growing team and organization. You will support building and scaling the team, define our technical vision and help shape the roadmap. Your team will lead the charge on multiple critical technical challenges: distributed training of foundation models, serving at scale, designing the user experience. You’ll work closely with sister teams in the AI platform organization ensuring a seamless AI development cycle. You’ll also partner with the Applied AI org and with Datadog infrastructure & tooling teams to build out systems from the ground up. 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: Manage and grow the Training & Serving team, directly managing 10+ engineers Define our technical roadmap in alignment with AI platform goals and the Applied AI team roadmap. Work with our core platform teams to tailor Datadog's storage, infrastructure and data pipelines to our needs Create a strong team culture aligned with our engineering standards and our customer focus Participate in hands-on work: Code reviews, design reviews and some coding Who You Are: Previous experience (1+ years) leading software engineering teams, as a tech lead or people manager Strong technician with a mix of backend, data engineer and infrastructure experience who is interested in remaining a hands-on leader Excellent leader with strong

About the Job Lead performance work that turns large-scale AI workloads into faster, more efficient systems. As a Staff AI Performance Engineer, you will analyze and optimize AI training and inference workloads across distributed systems. Your work will connect compute, memory, networking and software behavior. You will lead complex performance workstreams across hardware and software. You will find bottlenecks, validate improvements and turn performance evidence into practical engineering decisions. You will build benchmarks, profile demanding workloads and develop C++ and Python tools for analysis and optimization. Your recommendations will help teams improve efficiency, scalability and reliability. This role gives you broad technical scope across AI infrastructure at data center scale. It is based in Milpitas, California. The team and culture The System Engineering Performance team architects, evaluates and optimizes high-performance infrastructure for large-scale data center deployments. The team works across the computing stack to understand real system behavior. Work moves through evidence, ownership and clear technical judgment. You will lead investigations, coordinate across teams and validate impact with reliable performance data. Decisions are shaped through benchmarks, models, simulations and practical engineering tradeoffs. The team values engineers who think big, act fast, take responsibility, speak up and lead beyond their own area. What we’re looking for · Strong experience profiling and optimizing AI, machine learning or high-performance computing workloads. · Experience with distributed systems and communication libraries such as MPI, NCCL, UCX or libfabric. · Strong C++ and Python skills, including reliable tools or performance-sensitive software. · Deep understanding of compute, memory and communication behavior in large-scale systems. · Ability to lead technically complex work and coordinate improvements across teams. · Familiarity with MLPerf, a

The AI platform is responsible for all AI infrastructure across Datadog. Our mission is to provide tools and platforms that enable data scientists and engineers to conduct large-scale training and inference with ease. We support products such as Bits AI , LLMObs and all our AI research . As an engineering manager for the Evaluation & Annotation team, you’ll join a new and fast growing team and organization. You will support building and scaling the team, define our technical vision and help shape the roadmap. Your team will lead the charge on multiple critical technical challenges: AI model evaluation both offline and online, designing tooling and processes around human annotation, and establishing the standard around synthetics and AI generated datasets. You’ll work closely with sister teams in the AI platform organization ensuring a seamless AI development cycle. You’ll also partner with the Applied AI org and with Datadog infrastructure & tooling teams to build out systems from the ground up. 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: Manage and grow the Evaluation & Annotation team, directly managing 4-6 engineers Define our technical roadmap in alignment with AI platform goals and the Applied AI team roadmap. Work with our core platform teams to tailor Datadog's storage and data pipelines to our needs Create a strong team culture aligned with our engineering standards and our customer focus Participate in hands-on work: Code reviews, design reviews and some coding Who You Are: A Software Engineer at heart with a previous experience leading software engineering teams, as a tech lead or people manager Excellent leader with strong interpersonal skills, and the

What we're building Mutiny is the self-improving AI infrastructure for GTM teams to execute faster and close more revenue. Our ambition is to do for revenue velocity what Cursor and Claude Code did for engineering velocity. With Mutiny, everyone in sales and marketing gets a bench of GTM athletes that handle any work across their revenue motion and learn from what's actually moved their deals. In April we re-launched the product as an agent-first platform. Anthropic showcased us as a leader in AI GTM. MRR is growing more than 70% month-over-month, with customers like Uber, Rippling, and Snowflake. We're backed by Sequoia, YC, and Insight, and we're building a generational company. The opportunity Most engineers spend their career making predictable systems faster. You'll spend yours making non-deterministic ones trustworthy. As a senior engineer on our AI product team, you'll architect the Campaign Builder and Agent experiences marketers and sellers open every day to go from idea to personalized assets in minutes. You'll partner directly with product, design, and the founders to define what an agent-first GTM platform should feel like, and your calls on architecture, evals, and guardrails compound across thousands of customer accounts. This role is in person in New York City, five days a week, and we ship weekly. What you'll own The core agent surfaces. Architect and ship the Campaign Builder and Agent experiences end-to-end. Frontend, backend, prompts, evals, the whole stack. Reliability on top of LLMs. Make non-deterministic models feel deterministic at the surface. Build the retries, fallbacks, and orchestration so the customer never sees the failure mode. Evals and guardrails. Define how we measure quality, catch regressions, and keep brand and tone consistent across thousands of customer accounts. Speed and feel. AI products live or die by latency and the loop between intent and output. You'll obsess over both, and use coding agents and agent networks to ship f

Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brain of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As a NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. NVIDIA is looking for an AI Computer Engineer to join its NVIDIA Infrastructure Specialists (NVIS) team. Academic and commercial groups around the world are using NVIDIA products to revolutionize deep learning and data analytics, and to power data centers. Join the team building many of the largest and fastest AI factory systems in the world! NVIDIA is looking for someone with the ability to work on a dynamic customer-focused team that requires excellent interpersonal skills. This role will be interacting with customers, partners and internal teams, to analyse, define and implement large scale AI Factory projects. The scope of these efforts includes a combination of Networking, System Design and Automation while being the face to the customer. What you will be doing: Primary responsibilities will include deploying, managing, and maintaining AI infrastructure for new and existing customers. Be the domain expert with customers during planning calls through implementation. Handover-related documentation and perform knowledge transfers required to support customers as they begin rolling out some of the most sophisticated systems in the world! Provide feedback to internal teams such as opening bugs, documenting workarounds, and suggesting improvements. What we need to see: Bachelor’s degree in computer science, Electrical Engineering, or a related field, or equivalent experience.

Senior Engineering Manager – AI CoE About Redwood Redwood Software helps the world's largest enterprises automate mission-critical business processes. Our platforms support complex enterprise workloads across ERP systems, business applications, cloud infrastructure, and on-premises environments. Redwood is building reusable AI infrastructure, agentic capabilities, developer tools, and platform services that enable enterprise-ready AI features across its product portfolio. Role Mission As a Senior Engineering Manager, you will lead the engineering team responsible for delivering Redwood's shared AI platform and agentic capabilities. You will translate the AI strategy into an executable engineering roadmap, build a high-performing team, and ensure the delivery of secure, scalable, observable, and commercially valuable AI systems. The role requires sufficient technical depth to guide architecture and challenge engineering decisions while remaining accountable for people's leadership, execution, team health, and delivery outcomes. Key Responsibilities Lead one or more engineering teams responsible for AI platform services, agentic capabilities, developer tools, and product integrations. Translate business and product priorities into a clear engineering roadmap, delivery plan, and team-level objectives. Recruit, onboard, manage, and retain engineers with the skills required to build production-grade AI and agentic systems. Establish clear ownership, performance expectations, decision-making responsibilities, and development plans across the team. Drive predictable execution through effective planning, dependency management, risk identification, and operational readiness. Partner with senior individual contributors to guide architecture and technical direction without becoming a bottleneck for engineering decisions. Ensure consistent engineering practices across AI evaluation, observability, model gover

About Syllo Syllo is on a mission to transform litigation. Our product is a unified litigation platform that enables lawyers and paralegals to safely harness the power of language models and agentic AI throughout the litigation life cycle. Since going to market, we have gained a diverse group of enterprise customers, including some of the biggest law firms and corporations in the country, and we are quickly expanding. By reducing the expense of litigation industry-wide, we aim to improve access to high-quality representation and promote the alignment of legal outcomes with merit. About the Role As we continue to scale our AI platform, we're investing in our own inference stack to deliver best-in-class performance, reliability, cost efficiency, and flexibility across the latest generation of open-source language models. We're looking for a Staff Software Engineer to spearhead this effort. You'll define the architecture, evaluate emerging technologies, and build the systems that power model serving. You'll partner closely with machine learning, infrastructure, and product engineering to establish the foundation for how AI models are deployed, optimized, monitored, and operated in production. This is a highly hands-on technical role. You'll spend the majority of your time designing, building, and optimizing production systems while helping shape our long-term AI infrastructure strategy. Responsibilities Lead the design and development of our production inference platform. Define the technical roadmap for inference infrastructure, model serving, and runtime optimization. Build and operate scalable, cost-effective systems for serving large language models in production. Evaluate and integrate modern inference technologies, frameworks, and serving runtimes. Optimize latency, throughput, GPU utilization, memory efficiency, and infrastructure cost. Develop systems for model deployment, traffic routing, autoscaling, scheduling, observabil

About the Team OpenAI’s Hardware organization is building the infrastructure foundation for frontier AI systems. The Supply Chain organization is responsible for enabling scalable, resilient, and efficient supply chains across custom silicon, systems, networking, memory, storage, and rack-scale infrastructure. The Supply Chain Program Manager (SCPM) drives end-to-end material readiness and supply chain execution across the hardware lifecycle, from early architecture and sourcing strategy through production ramp and sustaining operations. We are looking for experienced supply chain leaders who can operate across technical, commercial, and operational domains to ensure material readiness, supply continuity, and execution excellence for complex AI hardware programs. About the Role As a Supply Chain Program Manager, you will own material readiness and supply chain execution for critical hardware programs spanning custom silicon, systems, memory, storage, networking, and rack infrastructure. You will work cross-functionally with Engineering, Strategic Sourcing, Manufacturing Operations, Finance, Planning, Quality, and external suppliers to develop and execute scalable supply strategies that support aggressive product development and deployment timelines. This role requires deep understanding of hardware supply chains, material planning, NPI execution, supplier management, and operational scaling in constrained and rapidly evolving environments. Responsibilities Material Readiness & Supply Planning - Own end-to-end material readiness across NPI and production phases, including building the necessary framework and processes for enablement. Drive supply planning and execution for long lead-time and constrained commodities including ASICs, HBM, DDR, SSDs, networking, optics, power, thermal, and mechanicals. Build and manage material readiness plans aligned to proto/pre-EVT, EVT, DVT, PVT, and mass production schedules. Monitor supply health, lead times, inventory positio

Principal Software Engineer – AI CoE About Redwood Redwood Software helps the world's largest enterprises automate mission-critical business processes. Our platforms support complex enterprise workloads across ERP systems, business applications, cloud infrastructure, and on-premises environments. Redwood is building reusable AI infrastructure, agentic capabilities, developer tools, and platform services that enable enterprise-ready AI features across its product portfolio. Role Mission As a Principal Software Engineer, you will lead the design and delivery of complex, production-grade AI and agentic capabilities that can be reused across Redwood products. You will solve ambiguous engineering problems, establish standards for reliability and AI evaluation, and contribute to important technical decisions. This is a hands-on role with ownership of significant systems and platform capabilities. Key Responsibilities Design and deliver complex services, agentic workflows, and shared AI platform capabilities. Build multi-step agentic systems that combine reasoning, tool use, retrieval, memory, policy controls, and human oversight. Establish robust approaches for AI evaluation, observability, model selection, fallback behaviour, and failure recovery. Lead technical delivery across multiple contributors and coordinate dependencies with product and platform teams. Review designs and code for scalability, security, performance, reliability, maintainability, and cost. Resolve complex production issues and implement corrective actions that prevent recurrence. Create reusable frameworks, reference implementations, and engineering standards that accelerate development. Partner with Product, Architecture, Security, Data, and Operations teams to translate business problems into durable platform solutions. Evaluate emerging AI technologies and recommend adoption based on technical evidence, enterprise requirements, and business

Opportunity: Get Well is seeking a highly skilled and proficient Staff AI Engineer to drive the development and implementation of advanced AI solutions. This role focuses on the technical execution of AI projects, leveraging expertise in Large Language Models (LLMs), multimodal AI systems, and agentic systems to create innovative healthcare solutions. You will work closely with AI engineering teams, product teams, and cross-functional stakeholders to translate technological capabilities into impactful healthcare solutions, serving as a key technical liaison. Responsibilities: AI Solution Development Design, develop, and implement AI solutions leveraging unimodal and multimodal Large Language Models (LLMs) and agentic systems. Develop and integrate agentic systems using frameworks like CrewAI, LangChain, and LangGraph to enable autonomous decision-making and workflow automation. Enhance product ecosystems by incorporating emerging AI technologies to improve functionality and user experience. Execute model training, fine-tuning, and optimization of inference engines for high-performance AI systems. Ensure technical excellence in AI solution design and implementation. Evaluate and integrate emerging AI technologies into existing systems. Cross-Functional Collaboration Serve as primary technical liaison between AI engineering teams and product teams Translate technical capabilities into product requirements and user-centric solutions Help facilitate communication and alignment across engineering, product, and clinical teams Drive collaborative innovation and knowledge sharing Infrastructure and Optimization Optimize cloud-based AI infrastructure for scalability, performance, and reliability. Implement best practices for efficient AI system deployment and maintenance, including agentic workflows. Performance and Quality Assurance Establish and maintain rigorous testing and validation protocols for AI models Drive continuous improvement in AI model accuracy, efficie