USAPlatform / Site Reliability Engineer
2 нед. назад
USA
Responsible for operating and ensuring reliability of customer-facing SaaS products and related systems.
Обязанности
- operates customer-facing SaaS products, connector and ingestion services, asynchronous workers, high-volume data pipelines, model-backed systems, review tools, and customer-delivery paths. These workloads have different shapes, but they need a coherent foundation for infrastructure, delivery, observability, recovery, access, and cost.
- You will build and operate the shared platform that lets our product, data, and AI teams ship reliable, secure, observable, and cost-aware systems without manual infrastructure work or operational risk growing linearly. You will write software and infrastructure, improve real engineering workflows, lead through incidents, and create paved roads teams can use without waiting on you.
- This is not a deployment-operator or internal-IT role. Product, data, and ML teams remain responsible for the systems they build. You will give them the runtime, delivery, visibility, recovery, and operating patterns to own those systems well. You will partner closely with our Security Lead, but you will not be expected to run the entire security or compliance program.
- Establish 's current platform, workload, reliability, ownership, toil, recovery, cost, and technical-control baseline
- Build reusable infrastructure-as-code modules, runtime templates, deployment workflows, environment contracts, and operational tooling
- Create supported paths for customer-facing services, asynchronous and batch jobs, data pipelines, and model-backed workloads
- Improve deploy safety, workload visibility, backup and recovery, incident response, replay, rollback, and durable remediation
- Work with engineering teams to define useful service and pipeline objectives, ownership, escalation, and recovery paths
- Build self-service for common infrastructure, environment, access, deploy, debugging, and recovery work without becoming a central approval queue
- Make cloud and vendor cost understandable by service and workload and improve efficiency within explicit reliability and security bounds
- Partner with Security on cloud identity, secrets, isolation, audit logging, vulnerability response, incident readiness, and automated control evidence
- Support employees and contractors through bounded access, safe environments, release controls, documentation, and timely removal of authority
- Use AI tools deeply for platform engineering and operations while verifying generated code, plans, queries, state changes, and incident conclusions
Будет плюсом
- Experience as an early platform or SRE hire at a fast-growing company
- Experience with AWS, Terraform, container runtimes, workflow orchestration, and observability systems
- Experience with high-volume data processing, model serving, evaluation jobs, GPU workloads, or machine-learning platforms
- Experience improving developer environments, preview systems, CI/CD, progressive delivery, or internal developer platforms
- Experience with replayable pipelines, backup and restore, disaster recovery, capacity planning, or cloud-cost allocation
- Experience implementing technical controls and automated evidence for SOC 2 or enterprise customer requirements
Условия
- We have scaled from $0 to a multi-eight-figure run rate in a matter of months
- We have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle Fund
- We are small enough that you will carry outsized responsibility and grow as quickly as the company does
- You will partner with and build for some of the fastest and most important companies in the world
- You will help build a massive, category-defining business from the ground floor
Другое
- At its core, was founded to help founders. We started by supporting startups through shutting down, but we have since expanded into unlocking a new revenue stream for all types of businesses.
- In 2025, we had a unique insight: the data every company generates each day through collaboration, communication, and building is some of the most valuable training data in the world. Public and synthetic data can only get frontier models so far, so the next generation of model progress depends on real, proprietary data grounded in how actual businesses operate. We are a primary source of it, partnering directly with the frontier AI labs building what comes next.
- Create a small set of supported patterns for customer-facing services, connectors, scheduled jobs, data-processing pipelines, model-backed workloads, and evaluation runs. Define the contracts for environments, compute, state, networking, delivery, secrets, telemetry, failure handling, and recovery without forcing every workload into an inappropriate stack.
- Make it straightforward for an engineer to create an environment, ship a safe change, understand a failed deploy or job, get the right access, recover a system, and know who owns the result. Build useful self-service and escape hatches while making unsupported paths and exceptions explicit.
- Connect service, queue, job, pipeline, and model telemetry to the outcome that matters. Establish practical objectives, alerts, incident mechanics, replay and recovery paths, and reviews that remove recurring failure classes instead of only documenting them.
- Expose cost and capacity in workload-relevant units, then improve them without hiding reliability, security, quality, or developer time. Work with Security to implement least privilege, secrets, logging, backup, deployment, and audit controls whose evidence comes from the systems that actually enforce them.
- 's environments, runtimes, deploy paths, service and pipeline owners, reliability risks, recovery gaps, manual work, and infrastructure costs are visible and prioritized
- One consequential failure or toil class is materially reduced in your first 90 days, and another team can use the resulting paved road without case-by-case help
- Product, data, and AI teams can ship and understand their systems faster while retaining clear operating ownership
- Priority services and pipelines have useful objectives, actionable telemetry, tested recovery paths, and incident learning that removes recurring failures
- Common platform work becomes self-service while exceptions remain explicit, owned, monitored, and time-bounded
- Cloud cost and capacity are understandable in workload-relevant units and improve without hidden reliability, security, or developer-time regressions
- Security and customer-trust evidence becomes easier to produce because it reflects current technical controls
- You have personally owned production cloud infrastructure and delivery or reliability systems across multiple services, including an asynchronous, batch-data, or model-backed workload
- You are a strong software engineer who is comfortable changing application, platform, and infrastructure code and operating the result in production
- You can reason from user impact through dependencies, state, telemetry, incident response, recovery, and durable remediation
- You have built paved roads other engineers adopted because they made real work easier, not because a platform team required them
- You understand both long-running services and high-volume or scheduled workloads and know where their reliability models should differ
- You can make pragmatic tradeoffs among delivery speed, least privilege, isolation, recovery, developer experience, and unit cost
- You are effective in an early-stage environment where the first step is often to establish ownership and a trustworthy baseline
- You can lead calmly through ambiguous incidents, communicate clearly, and leave the system and operating model stronger afterward
- You use modern AI engineering tools fluently and verify generated infrastructure, queries, code, and operational conclusions before they affect production
- You want a deployment or cloud-administration role where product teams hand systems to you to operate permanently
- You prefer designing a platform in isolation to learning how engineers, services, pipelines, and customer deliveries actually work
- You measure platform success by migration, ticket, dashboard, or uptime counts without connecting them to adoption, reliability, recovery, and user impact
- You want to standardize every workload on one stack regardless of its state, scale, failure, or recovery requirements
- You do not want AI tools to be part of your daily engineering and operational workflow