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Cerebras

Software Engineer, Inference Platform

Toronto, ON, Canada

Job type: Full-time
Apply by: Sep 30, 2026
Experience: 3+ years of experience in software engineering, with experience building and operating large-scale distributed systems or cloud infrastructure.
Posting language: English
Posted: Sep 23, 2026

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Original job description

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.

This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About The Role

We're hiring a Software Engineer to help contribute to projects on our Inference Platform team. Our team primarily owns the orchestration layer that runs inference on our datacenter clusters, connecting cloud components with machine learning services. We are often the first team to face problems that haven't been solved yet, leading solutions across Kubernetes operators, service security policies, and CI/CD.

If you're interested in building the next-generation architecture of a globally distributed inference platform, we'd like to talk.

Responsibilities

Design, develop, test, and maintain production software, with responsibilities spanning testing, continuous development, observability, security, networking, debugging, and productionization.
Platform Direction. Help shape the technical direction for the Inference Platform, Kubernetes custom resource definitions, failure domains, service boundaries, and system evolution over time, and own the roadmap for major technical areas.
Reliability & Performance. Architect active-active systems with rapid failover, graceful degradation, and clear SLOs. Drive system-level improvements in latency, throughput, capacity efficiency, and resilience under unpredictable demand.
Execution on Critical Paths. Write and review production code in the most important parts of the platform. Make high-consequence architectural decisions within your area and set the technical bar through design reviews, code reviews, and sound engineering judgment.
Production Leadership. Lead on the hardest production issues and cross-system bottlenecks. Drive observability, incident response, capacity planning, and post-incident improvement with a high standard for operational rigor.
Technical Influence. Partner with ML, Product, Infrastructure, and Cloud teams to translate product and business requirements into scalable system designs, and drive alignment on shared technical decisions within your domain and adjacent platform surfaces.

Skills & Qualifications

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

Preferred Skills & Qualifications

Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads.

Location: Open to Sunnyvale or Toronto.

Why Join Cerebras

About

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

Build a breakthrough AI platform beyond the constraints of the GPU.
Publish and open source their cutting-edge AI research.
Work on one of the fastest AI supercomputers in the world.
Enjoy job stability with startup vitality.
Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras here!

Apply today and become part of the forefront of groundbreaking advancements in AI!

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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More about this job

Responsibilities

Design, build, and operate the Inference Platform orchestration layer, including Kubernetes-based infrastructure, production software, and systems for reliability, performance, security, and observability. Lead technical direction and critical production work, make architectural decisions, and collaborate across ML, Product, Infrastructure, and Cloud teams.

Requirements

Requires at least 3 years of software engineering experience building or operating large-scale distributed systems or cloud infrastructure, ideally with Kubernetes. Candidates should have experience with highly available, latency-sensitive systems, security using certificates and TLS/mTLS, high-QPS performance optimization, and proficiency in Go or C++; ML inference or model-serving experience is preferred.

Skills

Distributed Systems
Kubernetes
Cloud Infrastructure
Highly Available Systems
Latency-Sensitive Systems
Security
TLS
mTLS
Latency Optimization
Throughput Optimization
Go
C++
ML Inference Infrastructure
Model Serving
Observability
Incident Response

Visa sponsorship

Not detected in the job text

Keywords

Inference Platform
Distributed Systems
Kubernetes
Kubernetes Operators
Cloud Infrastructure
Datacenter Clusters
Orchestration
Go
C++
TLS
mTLS
Certificates
Service Security Policies
CI/CD
Active-Active Systems
Failover
Graceful Degradation
Service-Level Objectives
Observability
Incident Response
Capacity Planning
Latency Optimization
Tail Latency
Time to First Token
Throughput
High-QPS Systems
ML Inference Infrastructure
Model Serving
GPU-Accelerated Workloads

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