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Added 26 mins ago
Sr SAP SuccessFactors Engineer
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Toronto, ON
Contract
The EMEA Senior SAP SuccessFactors Consultant is responsible for providing expert BAU support, L3 issue resolution, and continuous improvement for client SAP SuccessFactors landscape across…
Added 55 mins ago
Sr SAP SuccessFactors Engineer
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Sr. Staff Software Engineer, Inference Platform
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Full-time
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Added 1 hour ago
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$135,000 to $150,000 CAD / year
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Added 7 hours ago
Sr. Software Dev Engineer, Ring Cloud Connectivity Org
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Added 7 hours ago
Sr. Software Dev Engineer, Ring Cloud Connectivity Org
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Proven previous experience at the Senior Software Engineer role.
Added 14 hours ago
Sr. Electrical Engineer
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Added 1 day ago
Sr. Technical Systems Analyst – Database Activity Monitoring (GCS)
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Added 1 day ago
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Added 1 day ago
Sr. Infrastructure Engineer - Patching
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Added 1 day ago
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Added 1 day ago
Sr. Software Development Engineer in Test (Agentic)
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Cerebras

Sr. Staff Software Engineer, Inference Platform

Toronto, ON, Canada

Job type: Full-time
Apply by: Oct 1, 2026
Experience: 8+ years of experience in software engineering, with substantial individual contributor experience building and operating large-scale distributed systems or cloud infrastructure.
Posting language: English
Posted: Sep 23, 2026

Apply About employer

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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 Staff Engineer to help lead, drive, and 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.
Raise the effectiveness of senior engineers through design feedback, pairing, and clear technical standards.
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

8+ years of experience in software engineering, with substantial individual contributor experience building and operating large-scale distributed systems or cloud infrastructure.
Deep expertise in distributed systems architecture, ideally with Kubernetes.
Strong track record of making sound architectural decisions for 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++, with the expectation that you can contribute production code directly.
Experience designing observability and reliability practices, including metrics, logging, tracing, alerting, incident response, and SLO-driven operations.
Ability to influence senior engineers and cross-functional partners through technical credibility, communication, and judgment, especially within your domain and adjacent systems.
Preferred Skills & Qualifications
Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads.

Location: Sunnyvale or Toronto preferred

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.

This website or its third-party tools process personal data. For more details, click here to review our CCPA disclosure notice.

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More about this job

Responsibilities

Design, build, and operate the inference platform, including its Kubernetes orchestration layer, while shaping architecture, technical roadmaps, reliability, performance, and security. Lead critical production work and incidents, set engineering standards, and align with ML, Product, Infrastructure, and Cloud teams on scalable system designs.

Requirements

Requires 8+ years of software engineering experience, substantial hands-on work with large-scale distributed systems or cloud infrastructure, and deep expertise in distributed architecture, ideally with Kubernetes. Candidates should be proficient in Go or C++, experienced with highly available and latency-sensitive systems, security, observability, and SLO-driven operations; ML inference or GPU workload experience is preferred.

Skills

Distributed Systems
Kubernetes
Cloud Infrastructure
Go
C++
High Availability
Latency Optimization
Throughput Optimization
TLS
Mutual TLS
Observability
Reliability Engineering
Incident Response
Service Security
Machine Learning Inference
Model Serving

Visa sponsorship

Not detected in the job text

Keywords

Inference Platform
Distributed Systems
Kubernetes
Kubernetes Operators
Custom Resource Definitions
Cloud Infrastructure
Go
C++
Active-Active Systems
Failover
Graceful Degradation
Service Security Policies
Certificates
TLS
Mutual TLS
High-QPS Systems
Time To First Token
Tail Latency
Observability
Metrics
Logging
Tracing
Alerting
SLOs
Incident Response
Capacity Planning
CI/CD
Machine Learning Inference
Model Serving
GPU-Accelerated Workloads

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