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Weekend Heavy Duty Cleaner
FirstService Residential Canada
Toronto, ON
- Full-time
The job duties listed are typical examples of the work performed by positions in this job classification. Not all duties assigned to every position are included, nor is it expected that all…
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SP
Analyst/Sr. Analyst, Global Incentive Compensation
Salesforce Partner Canada
Toronto, ON
- Full-time
Communications: Communicate compensation program details with clarity and confidence, tailoring messaging for diverse audiences - from technical teams to senior leadership - to build…
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Small, Medium, and Growth Business - Account Executive - Vancouver (Office-Based)
Salesforce Partner Canada
Vancouver, BC
- Full-time
Added 8 mins ago
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Part-Time Sales Associate - Famous Footwear CAN
Caleres
Mirabel, QC
- Part-time
Benefit Offerings: PTO, Health, and Retirement for eligible full-time associates. To learn more details about our Total Rewards program, visit www.yourcaleres.ca.
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Security Supervisor/Superviseur de la sécurité
Playground
Kahnawake 14 Réserve Indienne, QC
- Full-time
Conduct on-the-job coaching and training for Security Officers to ensure compliance with protocols and service standards.
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T
Business Insights Analyst II
TD
Markham, ON · Toronto, ON · Montreal, QC
- Full-time
…may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational…
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Customer Experience Associate - (Mandarin and/or Cantonese)
TD
Markham, ON · Thornhill, ON · Toronto, ON
- Part-time
…may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational…
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Senior Group Manager, Finance Governance & Controls (Capital Markets)
TD
Toronto, ON
- Full-time
…may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational…
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Senior Personal Banker Trainee – Home Borrowing - Mandarin and/or Cantonese language skills considered an asset
TD
Coquitlam, BC
- Full-time
…may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational…
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Tooling SME, Product Delivery & Transformation
TD
Toronto, ON
- Full-time
…may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational…
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T
Customer Experience Associate
TD
Canmore, AB
- Part-time
…may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational…
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Customer Experience Associate
TD
Ottawa, ON
- Part-time
…may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational…
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PIA Client Service Associate
TD
Kingston, ON
- Full-time
…may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational…
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Senior Practice Consultant, Private Wealth Management
TD
Vancouver, BC
- Full-time
…may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational…
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Pharmacy Assistant
Cencora
Oakville, ON · CAN > ON > Oakville > Superior Crt
- Full-time
This job profile description is a standardized, non-contractual reference description and global reference tool provided for organizational consistency and talent architecture purposes…
Added 22 mins ago
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Dietary Aide Part Time
Maquise
Orleans, ON
- Part-time
…comply with all applicable pay transparency legislation. Further details regarding our total rewards offerings will be shared by the Talent Acquisition team during the hiring process.…
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Custodian Part Time Casual
ESFM (CCSSL Eurest)
Vancouver, BC
- Part-time
…comply with all applicable pay transparency legislation. Further details regarding our total rewards offerings will be shared by the Talent Acquisition team during the hiring process.…
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Cleaner Heavy Duty Full Time
ESFM (CCSSL Eurest)
Alliston, ON
- Full-time
…comply with all applicable pay transparency legislation. Further details regarding our total rewards offerings will be shared by the Talent Acquisition team during the hiring process.…
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Applications Development Senior Programmer Analyst
Citi
Mississauga, ON
- Full-time
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
Added 22 mins ago
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Lead Software Engineer - Enterprise Applications
Citi
Mississauga, ON
- Full-time
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
Added 22 mins ago
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Investment Banking Analyst - Metals & Mining
Citi
Toronto, ON
- Full-time
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
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Relationship Manager, Business Banking
CIBC
Saskatoon, SK
- Full-time
At CIBC we enable the work environment most optimal for you to thrive in your role. Details on your work arrangement will be discussed at the time of your interview.
Added 22 mins ago
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barista - Store# 04094, EAST POINTE
Starbucks Coffee Company
Saint John, NB
- Full-time
…opportunities for paid time off. Visit starbucksbenefits.ca for…
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barista - Store# 04747, CLAIR & GORDON (CLAIRFIELDS) - GUELPH
Starbucks Coffee Company
Guelph, ON
- Full-time
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Financial Services Representative - Universal Banker - Beaconsfield, QC
CIBC
Beaconsfield, QC · Saint-Louis, QC
- Full-time
You know that details matter. You notice things that others don't. Your critical thinking skills help to inform your decision making.

Cerebras

Software Engineer, GPU Inference

Toronto, ON, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Nov 8, 2026
Experience: 5+ years of software engineering experience, including substantial individual-contributor ownership of complex production systems.
Posting language: English
Posted: Oct 9, 2026

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

Cerebras is building a new generation of disaggregated AI inference systems that combine GPU-accelerated prefill with ultra-fast decode on the Cerebras Wafer-Scale Engine.

We are hiring a Software Engineer to productionize and optimize our GPU serving stack, working across our custom inference APIs, the vLLM serving runtime, the AMD ROCm software stack, and rack-scale AMD GPU infrastructure, to make this new serving path reliable, numerically correct, observable, and exceptionally performant.

You will write production code, establish operational practices for a new accelerator fleet, and drive improvements in time to first token, throughput, tail latency, and capacity efficiency. This is a hands-on role requiring deep debugging and optimization across application, runtime, distributed systems, and hardware layers.

Responsibilities

Productionize the GPU inference stack. Design, build, deploy, and maintain the complete GPU prefill path, spanning API services, model-serving workers, vLLM, PyTorch, ROCm, GPU nodes, networking, and rack-scale infrastructure.
Own GPU operational readiness. Establish deployment, upgrade, rollback, health-checking, capacity-management, and failure-recovery practices for the AMD GPU fleet. Build automation that makes driver, firmware, runtime, model, and container compatibility explicit and reproducible.
Drive reliability in production. Define service-level indicators and objectives for GPU-backed inference. Improve fault isolation, graceful degradation, automated recovery, incident response, and post-incident remediation across the serving stack.
Improve inference performance. Profile and optimize time to first token, request throughput, tokens per second per GPU, tail latency, GPU utilization, memory efficiency, and rack-level capacity under representative production workloads.
Optimize model-serving behavior. Tune and improve scheduling, continuous batching, prefix caching, KV-cache management, tensor and expert parallelism, request admission, quantization, graph execution, and distributed communication.
Debug across system layers. Diagnose complex failures and performance regressions across application code, vLLM, PyTorch, ROCm/HIP, collective communication libraries, kernels, drivers, firmware, networking, and hardware.
Ensure numerical correctness. Build validation and regression infrastructure for model quality, numerical accuracy, precision changes, quantization, determinism, and compatibility across software and hardware releases.
Build performance and correctness infrastructure. Develop representative benchmarks, workload replay tools, profiling automation, release qualification, dashboards, and regression gates. Turn one-off investigations into repeatable engineering systems.

Minimum Qualifications

5+ years of software engineering experience, including substantial individual-contributor ownership of complex production systems.
Experience building, operating, or optimizing production inference systems for large language models, multimodal models, or similarly demanding GPU workloads.
Strong programming ability in C++ and Python, including experience with multithreading, concurrency, memory management, and performance-sensitive software.
Hands-on experience with a high-performance model-serving framework such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, or an equivalent internally developed system.
Strong understanding of GPU execution and performance, including asynchronous execution, memory movement, synchronization, kernel launches, communication overhead, and profiling methodology.
Experience debugging distributed systems across multiple layers rather than treating the serving framework or accelerator runtime as a black box.
Experience with Linux, containers, Kubernetes or comparable orchestration systems, observability, CI/CD, and operating latency-sensitive services in production.
Ability to design rigorous benchmarks, interpret noisy performance results, identify bottlenecks, and translate findings into production improvements.
Strong communication and technical leadership skills, with a demonstrated ability to drive ambiguous cross-functional projects to completion.
Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related discipline, or equivalent practical experience.

Preferred Qualifications

Experience with AMD Instinct accelerators and the ROCm ecosystem, including HIP, RCCL, rocprofiler, AMD SMI, AITER, hipBLASLt, Composable Kernel, or related libraries and tools.
Deep CUDA experience that demonstrates an ability to transfer GPU systems knowledge across accelerator platforms.
Experience modifying or contributing to vLLM, SGLang, PyTorch, Triton, TensorRT-LLM, or another open-source ML systems project.
Experience optimizing prefill-heavy or disaggregated prefill/decode inference architectures.
Understanding of KV-cache transfer, prefix caching, continuous batching, chunked prefill, request scheduling, and memory-aware admission control.
Experience with multi-GPU and multi-node inference, including tensor parallelism, pipeline parallelism, expert parallelism, RDMA, collective communication, and failure handling.
Experience optimizing Mixture-of-Experts or multimodal models.
Knowledge of GPU kernel optimization, operator fusion, graph capture, attention kernels, GEMM tuning, and communication/computation overlap.
Experience with reduced-precision inference and quantization formats such as BF16, FP8, FP4, INT8, or INT4, including validation of their numerical and model-quality effects.
Experience building numerical-comparison, determinism, model-validation, or performance-regression test systems.
Experience collaborating directly with accelerator vendors, framework maintainers, or open-source communities.

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

Productionize and operate the GPU prefill serving stack across APIs, vLLM, PyTorch, ROCm, GPU nodes, and rack-scale infrastructure, establishing deployment, reliability, and recovery practices. Optimize inference performance and capacity, debug issues across software and hardware layers, and build repeatable benchmarking, correctness, observability, and release-qualification systems.

Requirements

Requires at least five years of software engineering experience, substantial ownership of production systems, experience with production LLM or comparable GPU inference, strong C++ and Python skills, and hands-on experience with a high-performance serving framework. Candidates should understand GPU execution and distributed systems, have experience operating latency-sensitive services with Linux, containers, orchestration, observability, and CI/CD, and hold a bachelor’s degree in a related discipline or equivalent practical experience.

Skills

C++
Python
GPU Inference
vLLM
AMD ROCm
GPU Performance Optimization
Distributed Systems
Kubernetes
Linux
Model Serving
Performance Profiling
Continuous Batching
KV-Cache Management
Quantization
Numerical Validation
Production Reliability

Visa sponsorship

Not detected in the job text

Keywords

Cerebras Systems
GPU Inference
AMD Instinct
AMD ROCm
HIP
RCCL
vLLM
PyTorch
SGLang
TensorRT-LLM
Triton Inference Server
C++
Python
Kubernetes
Linux
Distributed Systems
Rack-Scale Infrastructure
Continuous Batching
Prefix Caching
KV-Cache
Tensor Parallelism
Expert Parallelism
RDMA
Quantization
BF16
FP8
GPU Kernel Optimization
Performance Profiling
Numerical Correctness
Inference Reliability

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