Cerebras

4 weeks ago

Software Engineer, Inference Platform

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Build and maintain production software for the Inference Platform team, which orchestrates inference on datacenter clusters by connecting cloud components with ML services. You’ll shape platform direction, define Kubernetes custom resource behaviors and system boundaries, and drive reliability through active-active architectures, rapid failover, SLOs, and incident-driven improvements. Partner with ML, Product, Infrastructure, and Cloud teams to translate requirements into scalable distributed system designs.

Location: Sunnyvale, Toronto

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Mid level

Key Responsibilities

•Design, develop, test, and maintain production software across testing, continuous development, observability, security, networking, debugging, and productionization.
•Help shape the technical direction of the Inference Platform, including Kubernetes custom resource definitions, failure domains, service boundaries, and long-term system evolution.
•Architect active-active systems with rapid failover, graceful degradation, and clear SLOs; improve latency, throughput, capacity efficiency, and resilience.
•Write and review production code on critical parts of the platform and set the technical bar through design and code reviews.
•Lead hardest production issues and cross-system bottlenecks, driving observability, incident response, capacity planning, and post-incident improvements.

Key Requirements

•3+ years of software engineering experience building and operating large-scale distributed systems or cloud infrastructure.
•Experience with distributed systems, ideally with Kubernetes.
•Experience building highly available, latency-sensitive systems at scale.
•Experience with security including certificates, TLS, and mTLS.
•Strong proficiency in backend or systems languages such as Go or C++.
•Experience optimizing latency, throughput, and efficiency in high-QPS systems, including TTFT and tail-latency reduction (preferred).

Experience:3+ yearsDistributed systemsCloud infrastructureKubernetesHigh-QPS systemsML inference infrastructureGPU-accelerated workloadsModel serving

Skills:Engineering judgmentOperational rigorCross-team collaborationTechnical influence

Tech Stack:KubernetesGoC++TLSMTLSCI/CDKubernetes custom resource definitionsDistributed systems

Company Brief

Cerebras

Designs and builds wafer-scale AI accelerators and systems for large-scale deep learning workloads, delivering specialized hardware and software to accelerate model training and inference for enterprises and research institutions.

Industry: Hardware Devices

Company Size: Large (251 to 1,000 employees)

Growth: Scaleup

Headquarters: Sunnyvale, United States

Founded: 2016

Website LinkedIn

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Cerebras

4 weeks ago

Software Engineer, Inference Platform

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: Sunnyvale, Toronto

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Mid level

Key Responsibilities

•Design, develop, test, and maintain production software across testing, continuous development, observability, security, networking, debugging, and productionization.
•Help shape the technical direction of the Inference Platform, including Kubernetes custom resource definitions, failure domains, service boundaries, and long-term system evolution.
•Architect active-active systems with rapid failover, graceful degradation, and clear SLOs; improve latency, throughput, capacity efficiency, and resilience.
•Write and review production code on critical parts of the platform and set the technical bar through design and code reviews.
•Lead hardest production issues and cross-system bottlenecks, driving observability, incident response, capacity planning, and post-incident improvements.

Key Requirements

•3+ years of software engineering experience building and operating large-scale distributed systems or cloud infrastructure.
•Experience with distributed systems, ideally with Kubernetes.
•Experience building highly available, latency-sensitive systems at scale.
•Experience with security including certificates, TLS, and mTLS.
•Strong proficiency in backend or systems languages such as Go or C++.
•Experience optimizing latency, throughput, and efficiency in high-QPS systems, including TTFT and tail-latency reduction (preferred).

Experience:3+ yearsDistributed systemsCloud infrastructureKubernetesHigh-QPS systemsML inference infrastructureGPU-accelerated workloadsModel serving

Skills:Engineering judgmentOperational rigorCross-team collaborationTechnical influence

Tech Stack:KubernetesGoC++TLSMTLSCI/CDKubernetes custom resource definitionsDistributed systems

Company Brief

Cerebras

Industry: Hardware Devices

Company Size: Large (251 to 1,000 employees)

Growth: Scaleup

Headquarters: Sunnyvale, United States

Founded: 2016

Website LinkedIn