{"schemaVersion":"jobsearcher.job.v1","id":"2db6e9268adb85d441d4fa0e","url":"https://jobsearcher.com/jobs/2db6e9268adb85d441d4fa0e","canonicalUrl":"https://jobsearcher.com/jobs/2db6e9268adb85d441d4fa0e","title":"Founding Infrastructure Engineer","description":"Rebuild Matterhaul's infrastructure and core systems from zero — AWS, Kubernetes, Golang, and the data and pipeline choices that the rest of engineering will build on for years. Founding infra seat with the architectural calls in your hands.\r\nFull-time\r\nMatterhaul is building the AI-native operating system for the physical goods supply chain.\r\nDistributors and manufacturers run on disconnected systems, manual re-entry, and tribal knowledge that never gets captured. The software that was supposed to fix that never did. We're changing that.\r\nMatterhaul sits above the systems these businesses already run — unifying their data, capturing the operational context legacy software misses, and deploying AI agents across quoting, order entry, procurement, dispatch, and customer updates. No rip and replace. Teams go live fast, and Matterhaul expands until it becomes the system the business runs on.\r\nThat's the wedge. The vision is bigger: a purpose-built, AI-native platform that doesn't just automate what ERPs do today — it does what they were never capable of.\r\nWe're a small team with deep roots in this space. Our founders grew up in the trades and spent careers building products for the physical world at Stripe, Verkada, and Cisco Meraki. We're based in San Francisco's SOMA/Transbay neighborhood, in-office four days a week, and we spend real time with the distributors and operators we build for.\r\nWe move fast, ship often, and build for the people who actually do the work.\r\nWhy nowThree things are true at once and rarely line up:\r\nAI has stopped being an experiment.A year ago this was a pilot conversation. Today, executives are being asked why they aren't deploying AI in operations.\r\nLegacy ERPs are no longer defensible.The customers know it. Their leadership teams are openly looking for a way out.\r\nThe supply‑chain trauma of the last five years is fresh.The companies that move physical goods lived through it in a way software companies didn't. They want visibility and automation, and they aren't going back.\r\nThe window is open. Windows like this don't stay open.\r\nWhy this role existsToday we deploy to Railway: Docker‑per‑service, healthchecks, no Kubernetes, no IaC, shared Supabase Postgres, no data warehouse, no managed queue. It got us here. It will not get us into a distributor's critical workflow at scale, and it will not carry us through a Series A.\r\nThe next phase of the company is the platform — one cohesive system, not five tools that share a login. AI that shines. Workloads that customers can't operate without. That requires infrastructure built for it.\r\nWe're hiring a founding infrastructure engineer to rebuild our infra and core systems from zero: AWS + Kubernetes, clean service boundaries, Golang for core systems, and a deliberate set of choices around databases, data warehousing, and processing pipelines. You'll set the platform direction the rest of engineering builds on for years.\r\nThis is a high‑leverage seat. You will not be re‑skinning a Helm chart someone else wrote.\r\nWhat you'll ownYear one, concretely:\r\nAWS landing zone— accounts, networking, IAM, KMS, secrets, CI/CD, cost controls. Codified in IaC (Terraform / Pulumi / CDK — your call, with rationale).\r\nKubernetes platform— EKS or equivalent, with the supporting bits (ingress, service mesh if warranted, autoscaling, image pipeline, GitOps). Make boring choices that work.\r\nService architecture— Carve crisp boundaries across what is today a mixed Golang + Node/Effect.ts service fleet (API gateway, GraphQL, workflow cluster, public catalog, durable streams, OpenFGA, telemetry, ElectricSQL proxy, healthcheck sidecar). Decide what stays, what merges, what gets rewritten. Push Golang as the language for core systems where it pays off — proxies, gateways, high‑throughput pipelines, latency‑sensitive paths — without forcing it where TypeScript/Effect is already a fit.\r\nData layer strategy— We're on Supabase Postgres with Drizzle today, plus pgvector for embeddings, S3 for blobs, Redis for rate‑limit state. You will make the call on:Primary OLTP path forward (managed Postgres? Aurora? read‑replica strategy? connection pooling? per‑tenant isolation patterns?)\r\nData warehousing(Snowflake, BigQuery, Redshift, ClickHouse, Iceberg‑on‑S3, something else) — we have none today and need one.\r\nVector / search— keep pgvector, or move to a dedicated store?\r\nCache & ephemeral state— formal Redis/ElastiCache tier vs. in‑process.Processing pipelines & async work— We currently run Effect.js workflow clusters plus a homegrown durable‑streams service for everything async. It works but it's bespoke. You'll evaluate whether to harden it, augment it, or migrate selected workloads to off‑the‑shelf orchestration (Temporal, Airflow, Dagster, Flink, Kafka +Connect/Debezium for CDC, etc.) and build the ETL/ELT path into the warehouse from scratch.\r\nObservability— We have Sentry, OpenTelemetry, and Statsig wired. You'll extend with metrics/dashboards (Grafana / CloudWatch / Datadog — your call), SLOs, on‑call rotation, and runbooks.\r\nReliability & ops— Backups, DR, multi‑AZ today and a credible multi‑region story for tomorrow. Capacity planning. Cost ceilings. Security hardening (we already use OpenFGA for ReBAC and Clerk for authn — extend, don't unwind).\r\nEngineering leverage— Local dev that works in 10 minutes. CI that's fast. Deploy that's safe. Paved roads so product engineers don't have to think about infra.\r\nYou will write code. You will also make the architectural calls and document the \"why\" so the team can keep building after each decision.\r\nWhat we're looking forMust have:\r\n7+ years building backend and infrastructure systems in production, including meaningful 0→1 platform work (not just operating someone else's platform).\r\nProduction Golang— not just side‑project Golang. You've shipped, debugged, and operated Golang services that real customers depend on.\r\nAWS at depth— VPC, IAM, RDS, S3, EKS/ECS, ALB/NLB, CloudWatch, Secrets Manager, KMS. You can make IaC (Terraform / Pulumi / CDK) decisions and live with them.\r\nKubernetes in production— not just kubectl apply. You've built, broken, and fixed clusters; you know when to reach for a service mesh and when not to.\r\nDistributed‑systems judgment— consistency models, idempotency, retries, backpressure, queueing, the failure modes of multi‑region. You've debugged at least one outage that taught you something you still cite.\r\nData infrastructure experience— picked and run an OLTP database at scale, built or operated an ETL/ELT pipeline, and stood up a warehouse (Snowflake / BigQuery / Redshift / ClickHouse / Iceberg‑on‑S3 / equivalent).\r\nPragmatism about build‑vs‑buy.You've replaced custom orchestration with managed services and the other way around, and can explain why each was right.\r\nClear writing.This role makes durable decisions; we want them legible.\r\nNice to have:\r\nMulti‑tenant SaaS at scale, including noisy‑neighbor and per‑tenant isolation work.\r\nFamiliarity with Effect.ts (we are heavy users) — or willingness to learn it; you don't need to love it, but you need to live with it for the parts of the codebase that stay TypeScript.\r\nExperience with Temporal / Airflow / Dagster / Flink / Kafka / Debezium / dbt at production scale.\r\nStreaming and CDC patterns; experience with Postgres logical replication.\r\nAI/ML infrastructure: vector stores, GPU‑backed inference, embedding pipelines, prompt/response observability.\r\nBackground in voice / telephony / document processing pipelines.\r\nHow we workSmall founding team — under ten people — building the system distributor sales, procurement, and dispatch teams will run on. You will be the most senior infra voice in the room.\r\nSan Francisco — 4 days a week in the office.Foundational platform decisions go faster at a whiteboard with the team in the room.\r\nWe ship to production frequently and trust each other to do it.\r\nWe write specs and architecture docs before big changes. We expect the same of you.\r\nCompensationBase:$200,000 – $260,000, depending on level and experience.\r\nEquity:0.5% – 1.5%. This is a founding‑engineer grant; the range reflects the spread between senior and staff/principal.\r\nHardware + AI coding budget and a real office in SF.#J-18808-Ljbffr","company":"Matterhaul","rawCompany":"matterhaul","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-11-04T03:07:19.123Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1244.00","title":"Network and Computer Systems Administrators","slug":"network-and-computer-systems-administrators"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Founding Infrastructure Engineer","description":"Rebuild Matterhaul's infrastructure and core systems from zero — AWS, Kubernetes, Golang, and the data and pipeline choices that the rest of engineering will build on for years. Founding infra seat with the architectural calls in your hands.\r\nFull-time\r\nMatterhaul is building the AI-native operating system for the physical goods supply chain.\r\nDistributors and manufacturers run on disconnected systems, manual re-entry, and tribal knowledge that never gets captured. The software that was supposed to fix that never did. We're changing that.\r\nMatterhaul sits above the systems these businesses already run — unifying their data, capturing the operational context legacy software misses, and deploying AI agents across quoting, order entry, procurement, dispatch, and customer updates. No rip and replace. Teams go live fast, and Matterhaul expands until it becomes the system the business runs on.\r\nThat's the wedge. The vision is bigger: a purpose-built, AI-native platform that doesn't just automate what ERPs do today — it does what they were never capable of.\r\nWe're a small team with deep roots in this space. Our founders grew up in the trades and spent careers building products for the physical world at Stripe, Verkada, and Cisco Meraki. We're based in San Francisco's SOMA/Transbay neighborhood, in-office four days a week, and we spend real time with the distributors and operators we build for.\r\nWe move fast, ship often, and build for the people who actually do the work.\r\nWhy nowThree things are true at once and rarely line up:\r\nAI has stopped being an experiment.A year ago this was a pilot conversation. Today, executives are being asked why they aren't deploying AI in operations.\r\nLegacy ERPs are no longer defensible.The customers know it. Their leadership teams are openly looking for a way out.\r\nThe supply‑chain trauma of the last five years is fresh.The companies that move physical goods lived through it in a way software companies didn't. They want visibility and automation, and they aren't going back.\r\nThe window is open. Windows like this don't stay open.\r\nWhy this role existsToday we deploy to Railway: Docker‑per‑service, healthchecks, no Kubernetes, no IaC, shared Supabase Postgres, no data warehouse, no managed queue. It got us here. It will not get us into a distributor's critical workflow at scale, and it will not carry us through a Series A.\r\nThe next phase of the company is the platform — one cohesive system, not five tools that share a login. AI that shines. Workloads that customers can't operate without. That requires infrastructure built for it.\r\nWe're hiring a founding infrastructure engineer to rebuild our infra and core systems from zero: AWS + Kubernetes, clean service boundaries, Golang for core systems, and a deliberate set of choices around databases, data warehousing, and processing pipelines. You'll set the platform direction the rest of engineering builds on for years.\r\nThis is a high‑leverage seat. You will not be re‑skinning a Helm chart someone else wrote.\r\nWhat you'll ownYear one, concretely:\r\nAWS landing zone— accounts, networking, IAM, KMS, secrets, CI/CD, cost controls. Codified in IaC (Terraform / Pulumi / CDK — your call, with rationale).\r\nKubernetes platform— EKS or equivalent, with the supporting bits (ingress, service mesh if warranted, autoscaling, image pipeline, GitOps). Make boring choices that work.\r\nService architecture— Carve crisp boundaries across what is today a mixed Golang + Node/Effect.ts service fleet (API gateway, GraphQL, workflow cluster, public catalog, durable streams, OpenFGA, telemetry, ElectricSQL proxy, healthcheck sidecar). Decide what stays, what merges, what gets rewritten. Push Golang as the language for core systems where it pays off — proxies, gateways, high‑throughput pipelines, latency‑sensitive paths — without forcing it where TypeScript/Effect is already a fit.\r\nData layer strategy— We're on Supabase Postgres with Drizzle today, plus pgvector for embeddings, S3 for blobs, Redis for rate‑limit state. You will make the call on:Primary OLTP path forward (managed Postgres? Aurora? read‑replica strategy? connection pooling? per‑tenant isolation patterns?)\r\nData warehousing(Snowflake, BigQuery, Redshift, ClickHouse, Iceberg‑on‑S3, something else) — we have none today and need one.\r\nVector / search— keep pgvector, or move to a dedicated store?\r\nCache & ephemeral state— formal Redis/ElastiCache tier vs. in‑process.Processing pipelines & async work— We currently run Effect.js workflow clusters plus a homegrown durable‑streams service for everything async. It works but it's bespoke. You'll evaluate whether to harden it, augment it, or migrate selected workloads to off‑the‑shelf orchestration (Temporal, Airflow, Dagster, Flink, Kafka +Connect/Debezium for CDC, etc.) and build the ETL/ELT path into the warehouse from scratch.\r\nObservability— We have Sentry, OpenTelemetry, and Statsig wired. You'll extend with metrics/dashboards (Grafana / CloudWatch / Datadog — your call), SLOs, on‑call rotation, and runbooks.\r\nReliability & ops— Backups, DR, multi‑AZ today and a credible multi‑region story for tomorrow. Capacity planning. Cost ceilings. Security hardening (we already use OpenFGA for ReBAC and Clerk for authn — extend, don't unwind).\r\nEngineering leverage— Local dev that works in 10 minutes. CI that's fast. Deploy that's safe. Paved roads so product engineers don't have to think about infra.\r\nYou will write code. You will also make the architectural calls and document the \"why\" so the team can keep building after each decision.\r\nWhat we're looking forMust have:\r\n7+ years building backend and infrastructure systems in production, including meaningful 0→1 platform work (not just operating someone else's platform).\r\nProduction Golang— not just side‑project Golang. You've shipped, debugged, and operated Golang services that real customers depend on.\r\nAWS at depth— VPC, IAM, RDS, S3, EKS/ECS, ALB/NLB, CloudWatch, Secrets Manager, KMS. You can make IaC (Terraform / Pulumi / CDK) decisions and live with them.\r\nKubernetes in production— not just kubectl apply. You've built, broken, and fixed clusters; you know when to reach for a service mesh and when not to.\r\nDistributed‑systems judgment— consistency models, idempotency, retries, backpressure, queueing, the failure modes of multi‑region. You've debugged at least one outage that taught you something you still cite.\r\nData infrastructure experience— picked and run an OLTP database at scale, built or operated an ETL/ELT pipeline, and stood up a warehouse (Snowflake / BigQuery / Redshift / ClickHouse / Iceberg‑on‑S3 / equivalent).\r\nPragmatism about build‑vs‑buy.You've replaced custom orchestration with managed services and the other way around, and can explain why each was right.\r\nClear writing.This role makes durable decisions; we want them legible.\r\nNice to have:\r\nMulti‑tenant SaaS at scale, including noisy‑neighbor and per‑tenant isolation work.\r\nFamiliarity with Effect.ts (we are heavy users) — or willingness to learn it; you don't need to love it, but you need to live with it for the parts of the codebase that stay TypeScript.\r\nExperience with Temporal / Airflow / Dagster / Flink / Kafka / Debezium / dbt at production scale.\r\nStreaming and CDC patterns; experience with Postgres logical replication.\r\nAI/ML infrastructure: vector stores, GPU‑backed inference, embedding pipelines, prompt/response observability.\r\nBackground in voice / telephony / document processing pipelines.\r\nHow we workSmall founding team — under ten people — building the system distributor sales, procurement, and dispatch teams will run on. You will be the most senior infra voice in the room.\r\nSan Francisco — 4 days a week in the office.Foundational platform decisions go faster at a whiteboard with the team in the room.\r\nWe ship to production frequently and trust each other to do it.\r\nWe write specs and architecture docs before big changes. We expect the same of you.\r\nCompensationBase:$200,000 – $260,000, depending on level and experience.\r\nEquity:0.5% – 1.5%. This is a founding‑engineer grant; the range reflects the spread between senior and staff/principal.\r\nHardware + AI coding budget and a real office in SF.#J-18808-Ljbffr","datePosted":"2026-11-04T03:07:19.123Z","dateModified":"2026-11-04T03:07:19.123Z","hiringOrganization":{"@type":"Organization","name":"Matterhaul","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2db6e9268adb85d441d4fa0e"},"url":"https://jobsearcher.com/jobs/2db6e9268adb85d441d4fa0e"}}