Software Engineer, Frontier Data Products

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.

Responsibilities

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

Requirements

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; 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

Nice to have

Experience with Temporal or similar workflow engines

Benefits

Bi-annual performance bonus structure; Generous equity grant vested over 4 years; Up to $15k Relocation bonus; $10K housing bonus (if you live within 0.5 miles of our office); $1.5K monthly stipend for meals; Free Equinox membership; $200 monthly laundry reimbursement; $200 monthly personal wellness reimbursement; Health, Dental, Vision insurance