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Remote, Oregon, United States
Remote only
About Tessera Labs
Tessera Labs is redefining how enterprises adopt and operationalize Artificial Intelligence. Backed by Foundation Capital and led by a world-class founding team, we build multi-agent AI systems that automate complex business workflows across platforms like SAP, Salesforce, Workday, Snowflake, Databricks, and more.
Our mission is simple but ambitious: bring real AI automation to the enterprise — with speed, precision, and measurable impact. We move fast, operate with extreme ownership, and build at the frontier of applied AI.
The AI Platform team builds and operates the foundational infrastructure that lets Tessera's multi-agent systems run reliably, securely, and consistently across customer environments. That spans the portable cloud foundation (Kubernetes, networking, automation) and the AI-specific layer on top of it: model serving, agent runtimes, evaluation and release tooling, and the internal CLIs and services our engineers use every day.
The work directly enables us to onboard and operate enterprise customers under strict security and compliance constraints while preserving developer velocity. It reduces integration risk, standardizes operations across hyperscalers, and makes sure product teams can ship AI features safely without infrastructure becoming a bottleneck.
As an AI Infrastructure Engineer, you'll design, build, and own the tooling and services that take Tessera's AI agents from a developer's laptop to production in customer clouds. You'll write real software in Python or Go — CLIs, controllers, and internal services — and run it on Kubernetes alongside LLM serving stacks like vLLM.
This is a hands-on IC role open at mid to senior level. Mid-level engineers will own well-scoped components end to end; senior engineers will also set technical direction, make architectural calls across the platform, and mentor others. We'll calibrate level based on experience and interviews.
AI infrastructure tooling
Build and maintain internal CLIs, SDKs, and services in Python or Go that let engineers provision, deploy, test, and debug AI agents across environments
Deploy, scale, and tune LLM inference using vLLM (and similar serving stacks), including GPU scheduling, autoscaling, batching, and cost/latency trade-offs
Build infrastructure for multi-agent systems: agent runtimes, tool and connector execution, sandboxing, state management, and safe rollout of new agent versions
Own MLOps workflows: model and prompt versioning, evaluation pipelines, artifact registries, and promotion from staging to production
Write Kubernetes operators and custom controllers (CRDs) that encode how Tessera's AI workloads are deployed and managed
Cloud platform and reliability
Design, build, and operate production-grade AI agent infrastructure on Kubernetes and Terraform
Support Tessera's portable infrastructure across Azure, Oracle Cloud, GCP, and AWS, with primary ownership of at least one cloud variant
Improve observability for AI systems: tracing of agent and LLM calls, token and GPU utilization, latency, error rates, and cost
Strengthen security and reliability for workloads running in customer environments under strict compliance constraints
Automation and developer experience
Design and own CI/CD for infrastructure, services, and model/agent releases, using GitOps and pipeline-as-code
Build self-service tooling that removes manual work: environment provisioning, secret rotation, dependency updates, compliance checks, and release management
Partner closely with AI, product, and platform engineers to unblock delivery
Champion an automation-first mindset: if something is done twice, it should be automated; if it's automated, it should be observable and maintained like production code
5+ years of experience in infrastructure, platform, or backend engineering (7+ years for senior)
AI infrastructure tooling: strong software engineering in Python or Go, with a track record of building and shipping tools, CLIs, or services that other engineers rely on
Hands-on experience running LLM workloads in production, including serving with vLLM or a comparable stack (TGI, Triton, SGLang, Ray Serve)
Experience building infrastructure for AI agents or LLM applications: orchestration, tool calling, evaluation, or agent runtimes
Practical MLOps experience: model/prompt versioning, evaluation and release pipelines, and observability for ML systems
Strong hands-on experience with Kubernetes, including writing or extending operators/controllers (Kubebuilder, Operator SDK, controller-runtime, or Kopf)
Strong hands-on experience with Terraform and at least one of Azure, Oracle Cloud, GCP, or AWS
Proven experience designing and owning CI/CD systems, GitOps-driven deployments, and release automation
Solid networking fundamentals
Proven experience deploying and operating real production systems
GPU infrastructure experience: NVIDIA GPU Operator, MIG, KServe, or Karpenter-style autoscaling for inference
Familiarity with agent frameworks and protocols (LangGraph, Model Context Protocol, OpenAI Agents SDK, or similar)
LLM observability tooling such as OpenTelemetry, Langfuse, or Arize
Experience with FluxCD, ArgoCD, or other GitOps controllers
Cloud security and DevSecOps: security gates, SBOM generation, and compliance checks in CI/CD
Deep networking expertise (service mesh, private connectivity, multi-cloud networking)
Open-source contributions to AI infrastructure or Kubernetes ecosystem projects
A passion for clean, unified infrastructure and a builder mindset
A builder who writes production-quality code, not just configuration
Curious about the fast-moving AI infrastructure landscape and eager to learn new tools
Comfortable owning and defending infrastructure decisions, with clear reasoning about trade-offs
Experienced in startups and/or big tech environments
Highly self-motivated with a strong ownership mindset
Someone who enjoys building foundational systems that other engineers rely on
Work on cutting-edge AI infrastructure with real enterprise impact
Own foundational systems at an early-stage, high-growth company
Collaborate with a sharp, execution-focused founding team
Shape how AI agents are deployed across the largest enterprise platforms in the world
