Infrastructure Engineer

• Own our cloud infrastructure across AWS and GCP — Kubernetes clusters, networking, databases (Aurora PostgreSQL, Redis, MongoDB Atlas), Cloudflare, and our CI/CD pipeline.
• Scale single-tenant deployments from a handful to many — each with distinct data retention, geographic, monitoring, and compliance requirements. Make a private cloud deployment a repeatable, low-overhead operation.
• Build our observability and incident response practice — proactive monitoring, alerting, SLA tracking, and structured post-mortems that make the whole team better at diagnosing and resolving issues.
• Drive compliance and security operations — ensure we follow through on the policies we've written (SOC 2, NIST AI framework, EU Cyber Resilience). Own disaster recovery exercises, database restoration drills, and security event monitoring (SIEM).
• Manage infrastructure cost and capacity — make smart decisions about where we run workloads (AWS, CoreWeave, Parasail), optimize spend, and plan capacity as usage grows.
• Improve developer experience — CI/CD pipeline performance, preview environments, local development tooling, and deployment confidence.
• Contribute to backend systems where infrastructure and application intersect — circuit breakers, inference routing, data connector infrastructure for enterprise customers bringing their own data.

• Lead the end-to-end design, development, deployment, and monitoring of a scalable, governed enterprise AI platform leveraging Amazon EKS and AWS native services (e.g., Bedrock, OpenSearch Serverless, KMS, VPC).
• Design and implement agentic AI workflows, specialized autonomous agents, and multi-agent systems using advanced LLM orchestration techniques and agent frameworks.
• Architect and manage secure integrations using the Model Context Protocol (MCP) to connect the AI platform with internal systems, vector databases, and third-party SaaS applications (e.g., Google Workspace, Slack).
• Build and enforce strict identity, authorization, and zero-trust token brokering flows leveraging Okta, Auth0, and custom JWT authorizers to ensure secure, least-privilege tool execution.
• Implement deterministic policy controls (e.g., Cedar policy engine) to enforce role-based access, approval gates, and human-in-the-loop checks at the API gateway level.
• Develop and maintain highly isolated, scalable containerized runtime environments (e.g., Kubernetes pods on Amazon EKS) for secure AI model execution, tool usage, and knowledge retrieval.
• Establish and maintain comprehensive audit trails and observability for all AI interactions, utilizing AWS CloudTrail and GenAI observability tools (e.g., OpenTelemetry) to track cost, latency, and tool calls.
• Collaborate with Product Management, Security, Regulatory, and business stakeholders to translate enterprise requirements into scalable, compliant AI infrastructure solutions.
• Troubleshoot and resolve complex technical issues involving cloud infrastructure, Kubernetes networking, network isolation (PrivateLink), and agentic workflows.
• Contribute to technology roadmaps, AI infrastructure strategy, and long-term platform evolution initiatives.
• Mentor engineers, software developers, and technical teams while promoting engineering excellence, infrastructure-as-code (IaC) best practices, and continuous improvement.
• Partner with Quality, Regulatory, Privacy, Security, and Compliance functions to ensure software and AI systems operate in accordance with applicable regulatory requirements and company policies.

Staff Software Development Engineer – Enterprise AI Infrastructure

• Design, build, and scale services that orchestrate Ray clusters across cloud and on-prem environments, supporting both VM-based and Kubernetes-based deployments
• Optimize control plane components for large-scale, distributed AI/ML workloads
• Build intelligent scheduling and resource management systems for heterogeneous compute clusters
• Develop features to enhance the reliability, performance, scalability, and observability of Anyscale-managed Ray workloads
• Support and optimize accelerator integration (e.g., GPUs, TPUs).
• Handle container image management and dependency resolution for distributed workloads
• Participate in code reviews, design and architecture discussions
• Provide on-call support, working closely with customer and field teams to troubleshoot infrastructure issues
• Collaborate with leading distributed systems and machine learning experts to push the boundaries of AI infrastructure

Software Engineer, Infrastructure

• Work on features to increase the speed and functionality of our trading infrastructure: whether it's new trading algorithms, exchange integrations, performance improvements, or new features for the platform
• Play an active role in executing our system architecture and roadmap
• Collaborate with our Product Management team to form a plan of attack for new features, optimize performance of services and write tests
• Grow to become an expert in understanding our entire trading systems and all associated services
• Use deductive reasoning to solve complex problems that potentially exist beyond the scope of your local environment

Senior Backend Engineer, Trading Infrastructure

• Architect, deploy, and operate Kubernetes clusters across AWS and Lambda's bare-metal datacenters.
• Build and maintain automation for cluster lifecycle management — provisioning, upgrades, and scaling.
• Own the reliability, performance, and security of Kubernetes workloads in production.
• Implement observability, logging, and alerting for clusters and critical workloads.
• Partner with product teams to design scalable, cloud-native services and CI/CD pipelines.
• Set the standards for resource management, networking, and RBAC across the platform.
• Lead incident response, root-cause analysis, and post-mortems for platform issues.
• Mentor engineers and raise the bar for platform engineering across the org.

Senior Platform Engineer – Core Infrastructure

• Build API-based products from the ground up. Design and implement low-latency, high-reliability APIs for leaderboards, models, and arenas.
• Solve hard streaming problems. Handle SSE/streaming responses across heterogeneous providers, including partial failure recovery, mid-stream fallback, and consistent response normalization.
• Ship enterprise-grade infrastructure. Build the systems enterprise customers expect: rate limiting, authentication, usage metering, cost attribution, audit logging, and SOC 2 compliance.
• Build deep observability. Instrument infrastructure with distributed tracing, latency breakdowns, token-level usage tracking, and real-time dashboards so customers (and we) can see exactly what's happening.
• Build AI-centered products. Integrate with our core evaluation platform, Arena data, and customer-specific benchmarks. Collaborate with the research team to turn novel ideas into full-featured products.
• Flex across the stack. Contribute to the backend of our Leaderboards and Evals platforms when needed, helping unify our public and private data architectures.

Infrastructure Engineer – TL

• Monitor, operate, and support production AI infrastructure platforms
• Investigate and resolve infrastructure, networking, hardware, and platform-related incidents
• Support NVIDIA GPU infrastructure and associated platform services
• Monitor and troubleshoot Kubernetes-based environments
• Investigate performance, availability, and reliability issues across infrastructure and platform components
• Collaborate with engineering teams, hardware vendors, Data Center personnel, and service delivery teams to resolve technical issues
• Participate in incident response, root cause analysis, and operational improvement activities
• Contribute to improvements in monitoring, observability, automation, and operational processes
• Maintain operational documentation, runbooks, and knowledge articles

AI Infrastructure & Platform Operations Engineer

• Be the hands-on technical owner of the systems and pipelines that make the security program run: SIEM and data-warehouse architecture, data ingestion and enrichment, and security tool engineering across IT, OT, cloud, and bare metal.
• Design, deploy, and operate security tooling and data infrastructure: log-source onboarding, detection engineering, alert tuning, and pipeline management across IT and OT.
• Architect and maintain the security data platform: ingestion, normalization, enrichment, and routing of telemetry from endpoints, network, cloud, OT/ICS, and physical security tooling.
• Build and manage integrations across EDR, vulnerability management, SOAR, CASB, and identity platforms.
• Embed security into the software development lifecycle: CI/CD pipeline security, secrets management, artifact integrity, and review of security-relevant changes.
• Build tooling for automated security-data collection and quality validation, and partner with network, infrastructure, and IT teams to keep instrumentation complete across multi-site facilities.

Security Engineer – Infrastructure

• Design, build, and operate the Kubernetes platform that powers GNI network automation, telemetry, and operations across data center, colocation, and cloud environments.
• Own the lifecycle management for GNI Kubernetes environments, including cluster onboarding, upgrades, capacity, availability, and recovery.
• Develop production-quality software and automation for cluster provisioning, validation, upgrades, remediation, and safe multi-cluster delivery through GitOps.
• Provide production support for network services hosted on the platform, working with Network Automation and service teams that retain ownership of application architecture, code, and features.
• Diagnose complex Kubernetes platform and hosted-service failures involving control-plane health, cluster networking, storage, scheduling, workload placement, and multi-cluster dependencies.
• Drive issues from initial signal through verified resolution.
• Define production-readiness and observability standards for the platform and hosted network services, including health signals, capacity, alerts, runbooks, and recovery.
• Participate in CFR’s production on-call rotation, including scheduled after-hours and weekend coverage.
• Lead incident response and recovery, then drive corrective actions to completion.

Senior Platform Engineer, Network Infrastructure

• Design, build, and operate the backend services and infrastructure that power Vinci’s simulation and inference platform.
• Own infrastructure-as-code with Terraform across our cloud environments, provisioning and managing services reliably and repeatably.
• Build and harden CI/CD pipelines (Jenkins or equivalent) so the team can ship with confidence and strong regression coverage.
• Maintain and evolve our build systems (CMake, pip/uv) across large Python and C++ codebases.
• Take early prototypes through iteration and hardening all the way to customer use, in modest iterative steps.
• “Close the gap” on pre-existing infrastructure where needed, and evolve core platform components as the product grows.

Backend Infrastructure Engineer

• Leads and participates in IT infrastructure projects, including the design, deployment, and maintenance of server systems, cloud services, and virtualization platforms.
• Ensures projects are completed on time and within budget.
• Oversees the deployment of IT infrastructure components, including Windows, Linux, and virtualized environments.
• Implements system updates, patches, and upgrades to ensure optimal performance and security.
• Works closely with the architecture team to design and implement scalable and secure IT infrastructure solutions.
• Ensures alignment with architectural standards and best practices.
• Develops and implements backup and disaster recovery plans to ensure data integrity and business continuity.
• Scripts in PowerShell for automation of routine tasks and configuration management.
• Understands coding and scripting languages to support infrastructure automation.
• Implements and manages infrastructure using IaC principles and tools like Terraform to automate deployment and configuration processes.
• Participates in Agile workflow methodologies, including sprint planning, daily stand-ups, and retrospectives to ensure efficient project delivery.
• Setups monitoring systems and tools to ensure the performance, availability, and reliability of our IT infrastructure.
• Responds to IT infrastructure-related issues and provides timely resolution to minimize downtime.
• Collaborates with other IT teams and vendors to troubleshoot complex problems and implement solutions.
• Maintains accurate documentation of IT infrastructure configurations, processes, and procedures.
• Generates regular reports on system performance, capacity, and security metrics.

• Design, build, and maintain cutting-edge AI infrastructure for real-time computer vision, LLM, LVM, and multimodal inference workloads.
• Build scalable systems for running state-of-the-art models across large volumes of video and sensor data.
• Optimize inference performance across latency, throughput, GPU utilization, reliability, and cost.
• Develop robust evaluation harnesses and benchmarking systems to measure model quality, system performance, regressions, and production readiness.
• Build infrastructure for continuous model evaluation, experimentation, and deployment.
• Partner with research scientists to productionize the latest advances in computer vision, LLMs, LVMs, RAG, and multimodal AI.
• Improve model-serving architecture including batching, caching, routing, quantization, model parallelism, and hardware utilization.
• Develop data engines and feedback loops for collecting training data, evaluating model behavior, and continuously improving AI performance.
• Create reliable observability, monitoring, and debugging tools for production AI systems.
• Help define best practices for deploying, evaluating, and operating AI systems in real-world enterprise environments.

Senior Software Engineer, AI Infrastructure - LVM Inference & Evaluation

• Design, build, and operate scalable database infrastructure for mission-critical production and AI systems.
• Scale relational, analytical, and vector data stores to support growing product, customer, and AI workloads.
• Improve database performance across latency, throughput, availability, reliability, durability, and cost.
• Own database architecture decisions around partitioning, sharding, replication, indexing, caching, query optimization, and data modeling.
• Operate tier-0 data services with strong reliability, observability, incident response, and disaster recovery practices.
• Build automation and tooling to improve database provisioning, migrations, monitoring, backups, failover, and capacity planning.
• Partner with AI teams to support data infrastructure needs for embeddings, vector search, retrieval workflows, training data, model evaluation, and analytics.
• Build low-latency data-serving patterns that power AI features in production
• Work closely with engineering teams to design data access patterns that are scalable, reliable, and performant.
• Identify bottlenecks in production systems and drive improvements across application, database, cache, and infrastructure layers.
• Define and enforce best practices for schema design, database usage, data lifecycle management, and operational safety.
• Help evolve our long-term data platform strategy as the company scales.

Senior Software Engineer, AI Data Systems, Database Infrastructure

• Build and maintain the infrastructure that powers reliable data collection at scale
• Improve success rates against sites with aggressive defenses
• Own the health, rotation, and cost efficiency of the proxy layer
• Push throughput and reliability under heavy, spiky, real-world load
• Debug hard, intermittent failures across the stack and make them stay fixed

• Design, build, deploy, and run internal tooling for large-scale AI training and inferencing platform built on top of cloud infrastructure.
• Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters.
• Engage in and improve the whole lifecycle of services—from inception and design through deployment, operation, and refinement.
• Support services before they become available through activities such as system design consulting, developing software tools, platforms, and frameworks, capacity management, and launch reviews.
• Maintain services once they are live by measuring and monitoring availability, latency, and overall system health.
• Scale systems sustainably through mechanisms like automation, and evolve systems by pushing for changes that improve reliability and velocity.
• Practice sustainable incident response and blameless postmortems.
• Be part of an on-call rotation to support production systems.

Senior AI Infrastructure Engineer – DGX Cloud

• Engineer Internal Security Platforms: Develop and maintain self-service platforms that allow security teams to manage secrets, identity perimeters, and security guardrails at scale.
• Automation and DevSecOps: Write production-grade services to automate complex security workflows, turning manual interventions into reliable, code-driven processes.
• Infrastructure-as-Code (IaC): Establish automated security guardrails and golden path modules that ensure our cloud resources are secure by default.
• Build the Security Data Backbone: Design, build, and operate high-scale data pipelines to ingest, normalize, and enrich security telemetry for our detection and response functions.
• Partner with platform and product engineering to integrate security requirements into the developer lifecycle without introducing friction.
• Influence the selection of our security stack, evaluating build vs. buy for core security tooling.

Senior/Staff Infrastructure Security Engineer

• Oversee technical strategy and architecture in ML Inference Platform services
• Design, implement, and scale critical engineering components and services to support ML inference and deployment
• Work across teams to understand product requirements, evaluate trade-offs, and deliver the solutions needed to build innovative products or services
• Advocate for and apply best practices when it comes to availability, scalability, operational excellence, and cost management
• Provide technical direction that influences the entire company

Principal Software Engineer – Machine Learning Infrastructure

• Deploy and manage environments in the public cloud
• Automation including Infrastructure as Code (IaC)
• Container Orchestration and Observability platforms
• Creating and managing internal developer and infrastructure portals
• Design, optimize and manage our CI/CD pipelines
• Technical advocacy and support

Staff Software Engineer – Core Infrastructure, AI Compute

• Lead the technical direction for Snap’s Service Infrastructure platform
• Design, build, and operate foundational backend infrastructure for Snap
• Act as a hands-on Staff-level tech lead driving complex technical initiatives
• Partner closely with engineers and leaders across infrastructure and product teams
• Improve the developer and operator experience for teams building on Snap’s backend platform
• Drive best practices in service reliability, observability, incident response, system performance, and cost efficiency
• Mentor and uplevel other engineers while resolving complex technical conflicts
• Help shape how Snap’s infrastructure evolves for an AI-first future

Staff Software Engineer, Core Infrastructure

• Lead the technical direction for Snap’s Service Infrastructure platform, defining multi-quarter and multi-year roadmaps across areas such as service mesh, compute, observability, and adjacent platform capabilities.
• Design, build, and operate foundational backend infrastructure that powers critical services across Snap with a focus on reliability, scalability, performance, and operational excellence.
• Act as a hands-on Staff-level tech lead, driving ambiguous and high-impact technical initiatives from concept through architecture, implementation, rollout, and iteration.
• Partner closely with engineers and leaders across infrastructure and product teams to align on technical direction, priorities, trade-offs, and long-term platform investments.
• Improve the developer and operator experience for teams building on Snap’s backend platform by creating durable systems, abstractions, and mechanisms that raise engineering velocity and quality at scale.
• Drive best practices in service reliability, observability, incident response, system performance, and cost efficiency across mission-critical infrastructure.

Infrastructure Engineer jobs in California

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Infrastructure Engineer

Staff Software Development Engineer – Enterprise AI Infrastructure

Software Engineer, Infrastructure

Senior Backend Engineer, Trading Infrastructure

Senior Platform Engineer – Core Infrastructure

Infrastructure Engineer – TL

AI Infrastructure & Platform Operations Engineer

Security Engineer – Infrastructure

Senior Platform Engineer, Network Infrastructure

Backend Infrastructure Engineer

Infrastructure Engineer

Senior Software Engineer, AI Infrastructure - LVM Inference & Evaluation

Senior Software Engineer, AI Data Systems, Database Infrastructure

Backend Infrastructure Engineer

Senior AI Infrastructure Engineer – DGX Cloud

Senior/Staff Infrastructure Security Engineer

Principal Software Engineer – Machine Learning Infrastructure

Staff Software Engineer – Core Infrastructure, AI Compute

Staff Software Engineer, Core Infrastructure

Staff Software Engineer, Core Infrastructure