Staff AI Product Engineer

About the Role

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.

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

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

Preferred

• 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

Pay and benefits

The range below reflects the base salary for the position. Actual compensation may vary based on job-related factors such as skill set, experience, education, and location. In addition to base salary, this role may be eligible for bonus, equity, and/or commission programs. Nscale may offer a competitive benefits package including medical, dental, vision, flexible paid time off, parental leave, and retirement plan participation.

Salary Range $220,000—$330,000 USD

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