Platform Engineer
Role details
Job location
Tech stack
Job description
Now we are looking for an exceptional Platform Engineer to help us define and build the next generation of AI-powered logistics systems.
What You'll Do
You will build the infrastructure that our AI agents run on. We've made pragmatic early tooling choices for speed, and as we scale, some will hold and some won't. We need someone who understands the distributed systems principles underneath modern tooling, and can make the right call on when to keep things simple and when to reach for something heavier. This is a foundational role, and you'll design the systems the rest of the team builds on.
- Build and operate the runtime infrastructure that powers our AI agents in production, the systems that customers depend on
- Design for concurrency and failure, including queuing, retries, backpressure, and idempotency, so agent workflows stay reliable under real-world load
- Own production reliability: latency budgets, error budgets, capacity planning, and incident response
- Instrument the stack with tracing, structured logging, and alerting so problems surface before users notice them
- Own the deployment pipeline end-to-end: CI/CD, preview and staging environments, and the path from local dev to production
- Build internal tooling and abstractions that let the product team ship faster without worrying about infrastructure
Requirements
Do you have experience in MongoDB?, * Have experience owning infrastructure end-to-end at a startup, where you picked the tools and lived with the consequences
- Understand distributed systems deeply enough to know what managed platforms are doing under the hood, and where their abstractions will break
- Care about developer experience: deployment pipelines, local dev setups, error messages, and the small things that compound into engineering velocity
- Prefer to automate yourself out of toil rather than accept it
- Want to lay the foundation at a company where infrastructure decisions directly shape the product
What You Bring
- Strong understanding of distributed systems: concurrency, eventual consistency, event-driven architectures, and failure modes. You have the mental models even if you've implemented them with modern serverless tools rather than raw AWS primitives
- Experience with CI/CD design: multi-environment pipelines, automated testing gates, preview deployments, infrastructure-as-code
- Hands-on experience with platforms like Vercel, and an honest sense of where they scale and where they don't
- Familiarity with workflow orchestration tools like Inngest, Temporal, or Step Functions, and the queueing and retry semantics underneath them
- MongoDB experience: schema design tradeoffs, indexing strategy, and performance tuning as data and query patterns evolve
- Strong observability practice, ideally Datadog, including distributed tracing, custom metrics, and SLO-based alerting
- Enough AWS fluency (ECS, Lambda, SQS, IAM) to know what you'd reach for when the managed tools hit their limits
- High-agency mindset and comfort operating independently in a fast-changing environment