Product Engineer Jobs in Houston, TX

About Nscale

Nscale is taking on the hyperscalers by building a vertically integrated GenAI cloud platform. We own the data centres, software, and applications that power today's AI stack using sustainable technology solutions. We thrive on a culture of relentless innovation, ownership, and accountability, where every team member takes pride in their work and drives it with excellence and urgency. As a Nscaler, you'll build trust through openness and transparency, where everyone is inspired to do their best work. Collaboration is key, and we work together swiftly and respectfully, embracing adaptability and resilience in all we do.

About the Role

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.

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

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

Preferred

• 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