Jobs in Canada

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Added 1 day ago
CS
Cluster Operations Software Engineer
Cerebras Systems
Toronto, ON
- Full-time
- Hybrid
We are seeking a highly skilled and experienced AI Cluster Operations Engineer to manage and operate our cutting-edge machine learning compute clusters. These clusters would provide the…
Added 1 day ago
CS
AI Inference Core - Senior Senior Software Engineer for DevOps
Cerebras Systems
Toronto, ON
- Full-time
- Hybrid
We are hiring a Software Engineer to build and operate the platform layer behind Cerebras engineering infrastructure.
Added 1 day ago
CS
CoDesign & NextGen Performance Engineer
Cerebras Systems
Toronto, ON
- Full-time
- Hybrid
- Develop tools and infrastructure to help visualize performance data collected from the Wafer Scale Engine and our compute cluster.
Added 1 day ago
CS
Software Engineer - Tools & Infrastructure / DevOps
Cerebras Systems
Toronto, ON
- Full-time
- Hybrid
Added 1 day ago
CS
Senior SDET, Inference Platform
Cerebras Systems
Toronto, ON
- Full-time
- Hybrid
Added 1 day ago
CS
Cloud Infrastructure Engineer
Cerebras Systems
Toronto, ON
- Full-time
- Hybrid
- Design, build, and operate secure, scalable cloud infrastructure and identity platforms in AWS and our own data centers.
Added 1 day ago
CS
Software Engineer, Inference Platform
Cerebras Systems
Toronto, ON
- Full-time
- On-site
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…
Added 1 day ago
CS
AI Inference Core - Software Integration Engineer
Cerebras Systems
Toronto, ON
- Full-time
- Hybrid
We are looking for a Software Integration Engineer to join the AI Inference Core team at Cerebras. This team sits at the intersection of AI infrastructure, distributed systems, compilers,…
Added 1 day ago
CS
ML Systems Performance Engineer
Cerebras Systems
Canada
- Full-time
- Develop tools and infrastructure to help visualize performance data collected from the Wafer Scale Engine and our compute cluster.
Added 1 day ago
CS
Software Engineer, GPU Inference
Cerebras Systems
Toronto, ON
- Full-time
- On-site
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…
Added 1 day ago
CS
Staff Software Engineer, Inference Platform
Cerebras Systems
Toronto, ON
- Full-time
- On-site
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…
Added 1 day ago
CS
FPGA Engineer
Cerebras Systems
Toronto, ON
- Full-time
- Hybrid
Added 1 day ago
HC
Perception Engineer
Human Computer Lab
Toronto, ON
- Full-time
- Hybrid
We are looking for a hands-on Perception Engineer who can build the system that allows our robot to understand people and the world around it. You will work on developing computer vision…
Added 1 day ago
HC
Intern - Software/ML Engineer
Human Computer Lab
Toronto, ON
- Internship
- Hybrid
Added 1 day ago
HC
ML Engineer
Human Computer Lab
Toronto, ON
- Full-time
- Hybrid
This role is for a machine learning engineer who wants to work on the models that give LeLamp its intelligence – the systems that let the robot perceive its environment, understand what's…
Added 1 day ago
PS
GTM Engineer
Plane Software, Inc.
Canada
- Full-time
- You can connect systems yourself SQL, APIs, webhooks, and enough code to join a data layer to a CRM to a sequencer without waiting on an engineer.
Added 1 day ago
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Senior Product Security Engineer
LanceDB
Canada
- $180,000 to $250,000 USD / year
- Full-time
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of…
Added 1 day ago
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Software Engineer, Security Agents
Cohere
Canada
- $215,000 to $325,000 USD / year
- Full-time
- Hybrid
…security? The North Security team is looking for a Senior Software Engineer to create autonomous systems that handle the complex, critical tasks security practitioners face every…
Added 1 day ago
C
Member of Technical Staff, North Modelling (Evals)
Cohere
Toronto, ON
- $295,000 to $535,000 CAD / year
- Full-time
- Hybrid
…their real workflows while maintaining control over sensitive data. North connects AI agents with workplace tools, applications, and business context, helping users delegate complex work,…
Added 1 day ago
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Staff Software Engineer, Agentic Analytics
Cohere
Ottawa, ON · Toronto, ON · Montréal, QC
- $270,000 to $325,000 USD / year
- Full-time
- Hybrid
This is a hands-on staff engineering role reporting to our Senior Director of Data Analytics. You'll write production code and evaluation sets yourself while managing a small founding team…
Added 1 day ago
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Staff Software Engineer, Inference Infrastructure
Cohere
Toronto, ON · Montréal, QC
- $225,000 to $325,000 USD / year
- Full-time
- Hybrid
Added 1 day ago
A
AMI Research Engineer
AMI
Montréal, QC
- Full-time
- On-site
…modeling. We believe that video is a rich and abundant source of data reflecting how the world works, and that in general models need to be able to process continuous, high-dimensional data…
Added 1 day ago
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Principal Software Engineer
Passage
Toronto, ON
- Full-time
- On-site
Candidates must be based in Toronto or open to relocating and working five days per week in our Toronto office
Added 1 day ago
Manager, Software Engineering
Motus
Toronto, ON
…across their workforce. With solutions purpose-built to enable data-driven insights and strategic decision making, Motus is the preferred vehicle reimbursement partner to top Fortune 500…
Added 1 day ago
GC
Gameplay Engineer (Unreal Engine)
Game Companies
Vancouver, BC
- Full-time
Create modular, data-driven gameplay systems that support rapid iteration and scale across console and PC platforms.

Cerebras Systems

Software Engineer, GPU Inference

Toronto, ON, Canada

On-site

Full-time

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Job details

Job type: Full-time
Workplace: On-site
Experience: 5+ years of software engineering experience, including substantial individual-contributor ownership of complex production systems.
Posting language: English
Posted: Nov 25, 2025

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

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

The Software Engineer at Cerebras Systems will build, deploy and run the GPU serving path for AI models. They will also improve speed, reliability and numerical accuracy, and debug problems across the software and hardware layers.

Requirements

Cerebras Systems needs candidates with 5+ years of software engineering experience, including production inference work for large models. Strong C++ and Python, hands-on serving framework experience such as vLLM, and deep GPU performance knowledge are required.

Skills

vLLM
ROCm
HIP
AMD Instinct
PyTorch
C++
Python
Kubernetes
Linux
Docker
RCCL
rocprofiler
AMD SMI
hipBLASLt
Composable Kernel
CUDA
SGLang
TensorRT-LLM
Triton Inference Server
KV-cache management
continuous batching
prefix caching
tensor parallelism
expert parallelism
quantization

Keywords

Software Engineer
GPU inference
inference serving
vLLM
ROCm
AMD Instinct
HIP
PyTorch
C++
Python
Kubernetes
Linux
LLM serving
model serving
prefill decode
KV cache
continuous batching
tensor parallelism
quantization
FP8

Seniority

Senior

Tools

vLLM
ROCm
PyTorch
Kubernetes
AMD Instinct GPUs
RCCL
rocprofiler
AMD SMI

Languages

English

Fields of study

Computer Science
Computer Engineering
Electrical Engineering

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Work requirements

Jobs in Canada