Software Engineer, Frontier Data Products

About the role

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.

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

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

What We're Looking For

• 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

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

Compensation Range

$130K - $500K