Staff AI Product Engineer

Nscale•Seattle, WA

3h•$220,000 - $293,333

About The Position

Nscale is looking for a Staff AI Product Engineer to drive technical direction across the AI product domain. Working across 2–4 teams, you’ll set architectural standards, resolve complex cross-cutting concerns, and create engineering leverage that accelerates the entire product organization. As a Staff engineer, you define how Nscale’s AI services platform is built — establishing the patterns, APIs, and systems that other engineers rely on to deliver reliably and at speed. Your scope spans the full platform layer: AI service capabilities, API gateway, developer experience, billing, identity, and the extensibility surfaces that enterprise and developer customers build on. Your decisions will have meaningful, lasting impact on platform scalability, developer experience, and product velocity.

Requirements

8–12 years of software engineering experience
Proven ability to set technical direction for a product domain, including cross-team architectural patterns
Deep expertise in API design, platform engineering, and large-scale distributed systems
Experience designing cloud services with clear control plane / data plane separation and cell-based architecture for horizontal scalability and blast-radius isolation
Experience building and operating developer-facing platforms used by large numbers of engineers or customers
Proven ability to define the provisioning contract other teams onboard onto: typed inputs, readiness semantics, published outputs, and declared dependencies between provisioned services
Experience with dependency-ordered composition across services owned by different teams — readiness gating, eventual consistency, and deciding what may be provisioned in parallel
Track record of setting versioning and compatibility policy for customer-facing configuration surfaces, and of sequencing change across the schema, controller, packaging, and deployment layers that must land in order
Sustained hands-on production ownership at scale — has carried on-call for systems they designed and fed that operational experience back into the architecture
Track record of raising stability across a domain: SLO and error-budget policy, incident review that produces systemic fixes, and reliability tracked as a measurable trend rather than per-incident firefighting
Experience making cost a first-class engineering signal: usage attribution, cost-per-unit visibility, and guardrails that keep spend predictable as the platform scales
Strong ability to resolve ambiguous, open-ended technical problems at system scope
Demonstrated ability to influence without formal authority — across teams, disciplines, and seniority levels
Track record of creating durable technical standards and practices adopted across an organization

Nice To Haves

Experience designing SDK and Terraform provider strategies that enable customers to extend and automate the platform
Track record of building extensible platform layers: plugin systems, API versioning strategies, client library design
Experience building AI/ML product platforms: inference APIs, fine-tuning UX, model management, evaluation tooling
Experience building self-service, paved-path onboarding so teams can ship new deployable units without platform-team involvement
Background in GPU cloud or compute platforms serving ML workloads; experience operating platforms with strict SLAs at scale

Responsibilities

Set technical direction for the AI product platform domain across multiple teams
Drive cross-team architectural decisions: service boundaries, API contracts, data models, and platform standards
Identify and lead systemic improvements — performance, reliability, cost, or developer experience — that create leverage across the organization
Resolve ambiguous, open-ended technical problems where the solution space is genuinely undefined
Coach and grow Senior AI Product Engineers across teams; raise technical capability broadly
Partner with product leadership and engineering managers to align technical strategy with business direction
Evaluate build-vs-buy decisions for key platform capabilities and drive them to clear conclusions
Represent the product engineering domain in cross-functional architecture reviews