Staff FE Engineer - Inference

We’re hiring a staff level full-stack Technical Lead (L6/L7) to own and scale critical parts of the Cerebras Developer Console — the primary interface developers and enterprises use to run and manage inference workloads.

This is a deeply technical, end-to-end role. You’ll build high-quality frontend systems (Next.js, TypeScript) and design backend services (GraphQL, Postgres, Redis) that power usage tracking, billing, quotas, and observability. The systems you build will operate at high scale, require careful data modeling, and balance real-time and batch processing. You’ll be expected to make strong architectural decisions and move quickly from idea to production.

You’ll join an existing, high-velocity team and take ownership of major platform areas such as billing, request logs, and metrics. This is not a “ticket execution” role — you’ll define problems, drive technical direction, and lead execution across the stack. The work directly impacts customer experience and revenue, and the expectations are correspondingly high.

As a Technical Lead, you’ll set the bar for engineering quality and execution. You’ll mentor engineers, drive design reviews, and push the team toward simple, scalable solutions. We’re looking for someone who thrives in fast-moving environments, operates with urgency, and is comfortable navigating ambiguity while shipping high-quality systems.

• Own a major area of the platform — take end-to-end responsibility for systems such as billing, usage tracking, quotas, request logs, or metrics.
• Build and evolve core systems — design and implement APIs, services, and UI that power the Developer Console and scale with growing customer usage.
• Make architectural decisions — define system boundaries, data models, and tradeoffs across real-time vs batch processing, performance, and cost.
• Drive projects from 0 → 1 → scale — take ambiguous problems, define solutions, and deliver them to production.
• Lead technical execution — break down work, align engineers, and ensure high-quality delivery across the stack.
• Improve system reliability and visibility — ensure systems are observable, debuggable, and production-ready.
• Partner with product and design — shape developer-facing workflows and experiences.

• Track record of ownership — you’ve led and delivered complex systems end-to-end, not just contributed components.
• Technical depth in backend systems — strong fundamentals in APIs, data modeling, and distributed systems; experience with high-scale or real-time systems is a plus.
• Full-stack capability — comfortable working across frontend and backend, with the ability to make pragmatic tradeoffs.
• Strong technical judgment — you make sound architectural decisions and know when to optimize vs move fast.
• Ability to operate without structure — you bring clarity to ambiguous problems and drive execution independently.
• High standards for quality — you care about correctness, maintainability, and long-term system health.
• Influence and mentorship — you elevate the team through design reviews, guidance, and leading by example.
• Bias for action — you move quickly, unblock yourself and others, and focus on impact.
• Experience in fast-moving environments — comfortable with shifting priorities and evolving scope.
• Experience — typically 8+ years of industry experience building and operating production systems.
• Education — Bachelor’s or master's in computer science (or equivalent practical experience).

Cerebras is redefining the speed and scale of AI inference. The systems we build power real-world production workloads, not demos.

• Work on systems that matter — you’ll build core platform infrastructure that directly impacts how customers run and scale AI workloads in production.
• Own meaningful surface area — this is not a narrow role. You’ll take ownership of critical systems (billing, usage, observability) that sit at the heart of the platform.
• Operate at the intersection of product and infrastructure — the work spans developer experience, distributed systems, and real-time data, requiring both strong engineering and product thinking.
• High impact, low bureaucracy — you’ll work in a small, high-performing team where decisions are made quickly and engineers have real influence on direction.
• Grow with the platform — as we expand from cloud offerings into broader infrastructure and cluster management, the scope and technical challenges will continue to grow.