Senior AI Product Engineer

Nscale•Seattle, WA

4h•$180,000 - $260,000

About The Position

Nscale is looking for a Senior AI Product Engineer to join our product engineering team. You’ll lead technical design and delivery of major features and subsystems — acting as the technical anchor for your team while raising the quality bar across everything the team ships. At this level, you own substantial portions of Nscale’s AI services platform: the API gateway, core AI service capabilities, billing and usage infrastructure, and the developer-facing SDKs and tooling that customers use to build on Nscale. You work on complex, multi-sprint projects and drive them to completion while mentoring the engineers around you. This role expands beyond delivery into something broader: you become a multiplier for the people around you. That means growing the engineers you work with, shaping how the team approaches hard problems, and taking greater ownership of the technical direction of what you build.

Requirements

5–8 years of software engineering experience
Deep expertise in backend or full-stack development at scale (Python, TypeScript, Go, or similar)
Proven experience designing and operating API platforms or developer-facing cloud services
Strong track record of owning and delivering complex, multi-sprint projects end-to-end
Experience with cloud-native architecture: Kubernetes, distributed systems, observability stacks
Experience building services with clear control plane / data plane separation; familiarity with cell-based or ring-based architecture patterns for cloud service scalability and fault isolation
Experience with declarative, reconciliation-based provisioning: desired-state controllers or workflows that are idempotent, converge after partial failure, and detect drift in customer-facing resources
Treats customer-facing configuration surfaces — input schemas, defaults, and deployment values — as versioned API contracts, with validation, documentation, and compatibility handled deliberately
Experience making long-running provision and teardown flows supportable: readiness signals, structured failure reporting at the boundary that owns the decision, and clear remediation paths for operators
Hands-on production ownership: on-call rotation, incident response, and debugging live systems under real traffic
Experience monitoring and tracking service stability over time — SLOs and error budgets, alerting tied to customer impact, and detecting reliability regressions before customers report them
Experience instrumenting usage and cost: metering consumption, attributing spend to tenants or workloads, and keeping unit costs visible to the team
Ability to communicate technical decisions clearly to product managers and engineering stakeholders
Sound engineering judgment: knows when to simplify, when to invest, and when to defer

Nice To Haves

Experience building or consuming SDKs, Terraform providers, or extensibility layers for cloud platforms
Familiarity with infrastructure-as-code tooling (Terraform, Pulumi) and how platform teams expose it to customers
Experience building AI-integrated product features (LLM inference APIs, fine-tuning UX, agentic workflows)
Experience modelling a deployable unit as a contract over its packaging artifacts — typed inputs, defaults, readiness checks, and published outputs that other services consume
Working knowledge of AI/ML infrastructure concepts: serving, evaluation, and model lifecycle management

Responsibilities

Lead technical design and end-to-end delivery for major product subsystems and complex features
Own architectural decisions within your team’s domain — from API contracts to deployment strategy
Set the quality standard: drive test coverage, observability, reliability, and incident response
Mentor more junior engineers through design reviews, code reviews, and regular pairing
Collaborate cross-functionally with AI platform, infrastructure, and product teams
Identify and proactively address technical debt and scalability constraints before they become blockers
Contribute to hiring by reviewing candidate submissions and participating in interviews
Produce clear design documentation that enables asynchronous decision-making across the team