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# Software Engineer, Frontier Data Products

- **Company:** Amazon.com, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $62,400.0 - $72,800.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software Debugging, Distributed Systems, Python (Programming Language), PostgreSQL, State Machines, Backend
- **Published:** August 30, 2026
- **Apply:** https://www.careerjet.com/job/us4c738a4ca0021e1cdcd15aeb6b7de3e5/eaa

## About the Role

Production backend experience with strong opinions about what ages well and why
Sharp instincts for system design, service boundaries, and where to put complexity - and where to refuse it
Fluency with the distributed systems toolkit: async workflows, queues, idempotency, retries, and long-running jobs as practice, not resume line items
Ability to take ambiguous product, operational, and ML constraints and turn them into a system that is clean and debuggable
Comfort working in Python on AWS with Postgres; experience with Temporal or similar workflow engines is a plus
You're likely someone who:
Gets frustrated by systems that are hard to debug and takes that personally enough to fix it
Has strong opinions about where state should live and can defend them in a design review
Moves fast but doesn't treat reliability as someone else's problem
Wants your work to have a short, visible line to outcomes that actually matter to customers

## Description

Frontier AI companies are increasingly bottlenecked on expert judgment and high-quality data workflows. This team builds the production systems that capture, coordinate, and validate that work at scale - directly between a customer request and the output that ships.
These are long-running, stateful systems. A single job can stay live for days, interleaving automated steps, model inference, and expert review. A step marked "done" can be reopened, re-reviewed, and redone - so "completed" is not always final, state has to tolerate late mutation, and correctness has to survive humans and models disagreeing with each other.
This is a backend systems and orchestration problem: distributed state machines, not pipelines. The architecture is not set. Early engineers will decide what it becomes, and the loop between "I shipped this" and "this mattered" is short.
What You'll Do
Design services and state models for multi-stage workflows that fan out across automated processing and expert reviewers, then reconcile results into a coherent whole
Build orchestration primitives - retries, failure recovery, idempotency, auditable state transitions - for jobs that run far longer than a request and can be partially redone after the fact
Integrate model inference into production workflows without sacrificing debuggability or human oversight
Build the APIs and tooling that let product, operations, and ML teams operate, debug, and trust these systems at scale
Own reliability and observability for workflows where a silent failure means a corrupted result, not just a 500
What Makes This Role Different
You are building the core infrastructure that sits directly between customer requests and the outputs that ship - not internal tooling, not a support system
This product area is young and strategically central; early engineers are deciding the architecture, not inheriting it
The inputs are non-deterministic by nature - you are building durable orchestration over humans and models that can disagree with each other on hour 40 of a multi-stage job
Day-to-Day
Moving fast on genuinely hard systems problems - ambiguity is the default, not the exception
Working closely with product, operations, and ML teams to translate a tangle of constraints into clean system design
Debugging complex stateful workflows where the failure surface spans automated steps, model calls, and human reviewers
Owning your systems end-to-end: design, ship, operate, improve


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