Jobs in Canada

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Added 1 month ago
Senior Java Software Developer
Randstad Canada
Toronto, ON
If you're a Java engineer who enjoys solving complex problems, working with real-time data and building technology at scale, this could be a great opportunity to get involved in a…
Added 1 month ago
RC
Platform RUN Engineer
Randstad Canada
Baltimore, NB
- $110,000 to $145,000 CAD / year
We are seeking an experienced DevOps/Cloud Engineer to manage production workloads, build CI/CD pipelines, and automate infrastructure provisioning in a 24/7 cloud environment. This role…
Added 1 month ago
RC
Senior DevOps Engineer | Terraform | Docker | Azure | Hybrid
Randstad Canada
Toronto, ON
- $120,000 to $130,000 CAD / year
The person who does well here is a hands-on cloud engineer with genuine Azure networking depth, not just pipeline experience. A lot of DevOps engineers can wire up GitHub Actions and run…
Added 1 month ago
Senior Fullstack Developer - General application
Stay22
Montréal, QC
Collaborer avec les équipes Platform et Data pour s'assurer que vos solutions respectent les standards de l'organisation en matière de sécurité, d'observabilité et de gouvernance des…
Added 1 month ago
Observability Specialist
Wave
Canada
You will own and evolve Wave’s observability platform across our Python backend, GraphQL API, Postgres and CockroachDB databases, Kubernetes workloads, cloud infrastructure, and on-premises…
Added 1 month ago
RC
Software Developers – Telemetry and Observability Platform
Randstad Canada
Kanata, ON
- $55.42 to $103.91 CAD / hour
Operating on a Hybrid schedule (4 days per week in office), we are actively recruiting for:
Added 1 month ago
CI
Senior Site Reliability Engineer
Coalition, Inc.
Canada
- $135,900 to $201,000 CAD / year
We are looking for a Senior Site Reliability Engineer to join our Platform SRE team. In this role, you will build and operate the infrastructure, tools, and "paved roads" that empower our…
Added 1 month ago
Staff Software Developer
Auvik Networks
Canada
Auvik delivers cloud-based network management software that gives IT teams instant visibility and control over their infrastructure - whether on-site, remote, or dispersed across global…
Added 1 month ago
Software Developer II
Auvik Networks
Canada
Auvik delivers cloud-based network management software that gives IT teams instant visibility and control over their infrastructure - whether on-site, remote, or dispersed across global…
Added 1 month ago
Site Reliability Engineer (Senior or Staff), Storage Layer Services (SLS)
MongoDB
Montréal, QC · Toronto, ON
Expertise in cloud infrastructure platforms, including AWS, Google Cloud Platform (GCP), or Azure
Added 1 month ago
Senior Software Engineer, Deployments
MongoDB
Toronto, ON
Platform Engineering is the department within SRE that is responsible for a range of critical infrastructure and operational functions that support the broader engineering organization.…
Added 1 month ago
Forward Deployed Engineer, Industry Solutions
MongoDB
Toronto, ON
…innovate at the speed of the market. We have redefined the data platform for the AI era, enabling builders to create, transform, and disrupt industries with software. MongoDB’s unified data…
Added 1 month ago
Staff Software Engineer, Metadata
Fivetran
Toronto, ON
We're looking for a staff software engineer to be a technical leader on our Metadata team, which owns the metadata layer of the dbt platform - the discovery API, catalog, lineage, and run…
Added 1 month ago
Senior Software Engineer - Orchestration
Fivetran
ON
We're looking for a senior software engineer to join our Orchestration team, which owns the control plane layer of the dbt platform - the scheduler, execution harness, and results…
Added 1 month ago
Staff Devops Engineer
hireVouch
Ottawa, ON
We’re seeking a hands-on Staff DevOps Engineer to own architecture, automation, and scaleout of our Google Cloud Platform infrastructure. You’ll work closely with software engineers to…
Added 1 month ago
Applicant Support Engineer
hireVouch
Toronto, ON
Experience with Kubernetes, Jenkins, distributed tracing, and cloud-based applications is required.
Added 1 month ago
Staff / Lead Data Engineer
hireVouch
Toronto, ON
Staff Engineer candidates may bring a stronger centre of gravity in backend and API systems or in data processing and platform engineering. For the Lead Engineer search, the client is…
Added 1 month ago
RC
Platform Engineer
Randstad Canada
Vancouver, BC
- $55.96 to $74.61 CAD / hour
Assignment Type: Hybrid (4 days per week onsite)
Added 1 month ago
Machine Learning Engineer (Canada)
Tiger Analytics Inc.
Toronto, ON
Knowledge on any of the cloud-native MLaaS offerings like AWS SageMaker, AzureML, or Google AI platform
Added 1 month ago
Team Lead, DevOps
AGFA
Waterloo, ON
…guide and mentor the team but also actively contribute as an engineer. You will help shape our cloud-native transformation, drive the adoption of GitOps…
Added 1 month ago
Data Platform DevOps Engineer - Senior Consultant
Deloitte
Montréal, QC
As a Data & AI Platform Engineer, you operate at the foundation of modern data and AI ecosystems - designing, deploying, and operating scalable, secure, and reliable platforms that enable…
Added 1 month ago
PG
Lead Full Stack Developer
Publicis Groupe Holdings B.V
Toronto, ON
- $130,000 to $150,000 CAD / year
…builder and you hold the architectural picture of a multi-tier platform - React frontend, Spring Boot backend, FastAPI agentic services, agent SDKs on top of LLM providers. You are equally…
Added 1 month ago
Staff Database Engineer
ServiceNow
Toronto, ON
The candidate must have a passion for chasing issues of complex systems running under dynamic, real-world loads
Added 1 month ago
Senior Associate Data Engineering (Azure/Databricks) Hybrid
Publicis Groupe Holdings B.V
Toronto, ON
Hands-on experience with at least one leading public cloud data platform: Amazon Web Services, Microsoft Azure, or Google Cloud Platform;
Added 1 month ago
Senior Network Platform Engineer (AWS)
Capgemini
Toronto, ON
Must have skills:

Cerebras Systems

Software Engineer, GPU Inference

Toronto, ON, Canada

Apply on jobs.ashbyhq.com About employer

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Job details

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.

Apply on jobs.ashbyhq.com About employer

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

Department

software_development

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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Jobs in Canada