Find work in Canada.

621,957 jobs across our Canadian network. How we count

Jobs in Canada

Email me new jobs Save this search

Added 4 months ago
PI
Data Platform Engineer
Princeton IT Services, Inc
Toronto, Ontario
- Contract
Collaborate with internal stakeholders to translate business and analytical requirements into well-defined backend features and data products.
Added 3 months ago
PR
Senior Robotics and Control Engineer
Promise Robotics
Calgary, AB
- Full-time
We’re looking for a Senior Robotics and Control Engineer to join our team and contribute to developing cutting-edge solutions for our core robotic manufacturing technologies. This role is…
Added 2 months ago
A
Software Engineer (C++) - Platform & Emerging Technologies
Autodesk
Toronto, ON · Montreal, QC
- Full-time
Software Engineer (C++)– Platform & Emerging Technologies
Added 3 months ago
D
Software Engineer - Canada
DataVisor
Remote, Canada
- Full-time
- Remote
…delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's solution scales infinitely…
Added 3 months ago
O
Senior Data Engineer
OMERS
Toronto, ON · Head Office Toronto
- Full-time
Reporting to the Manager, Software Engineering, the Senior Data Engineer works across investment teams including Capital Markets, Infrastructure, Private Equity, Private Capital and…
Added 4 months ago
S
RevOps Engineer
Studeo
Remote, Canada
- $80,000 to $120,000 CAD / year
- Full-time
- Remote
A GTM Engineer's Toolkit - Clay and n8n: You build. You've created Clay tables with real enrichment waterfalls, AI columns, API calls, and write-backs into a CRM - and you can show us one…
Added 4 months ago
E
FPGA Design Engineer
Evertz
Burlington, Ontario
- Full-time
The FPGA Design Engineer at Evertz contributes to the development of advanced broadcast equipment used in real-time video and audio production, processing, and transport for Live sports…
Added 4 months ago
E
Senior Software Engineer - Backend
Evertz
Burlington, Ontario
- Full-time
As a Senior Software Engineer, you will combine strong software engineering fundamentals with modern AI-assisted development practices. You will work on the evertz.io microservices and…
Added 4 months ago
E
DSP Engineer (Markham)
Evertz
Markham, Ontario
- Full-time
Experience with real-time systems design on FPGA, CPU and GPU based platforms
Added 4 months ago
E
Junior FPGA Design Engineer
Evertz
Markham, Ontario
- Full-time
Experience with Python, Tcl, and other scripting languages
Added 4 months ago
E
Embedded Software Engineer
Evertz
Burlington, Ontario
- $60,000 to $90,000 CAD / year
- Full-time
We're looking for a passionate Embedded Software Engineer to join our development team, where you'll play a key role in designing, implementing, and maintaining embedded software for…
Added 4 months ago
SI
R&D Engineering, Staff Engineer
Synopsys Inc
Vancouver, British Columbia
- Full-time
You work well with people who think differently than you do, whether that is a physicist focused on theory or a software engineer focused on performance
Added 4 months ago
C
Jr Data Engineer - Custom Solutions
Citylitics
Toronto, Ontario
- $70,000 to $80,000 CAD / year
- Full-time
…reliable sales intelligence. The platform integrates seamlessly with CRMs, ERPs, and data lakes, embedding actionable insights directly into sales workflows so teams can prioritize the…
Added 4 months ago
K
Senior Software Engineer, Agents
Klue
Vancouver, British Columbia
- $140,000 to $196,000 CAD / year
- Full-time
Strong software engineering fundamentals. You write clean, maintainable, well-tested code. You're comfortable with Python and have experience with backend frameworks, APIs, and production…
Added 3 months ago
Forward Deployed Engineer, Supply Chain
Jetson Home
North Vancouver, BC
We're building a new kind of role to help us scale: the Forward Deployed Engineer (FDE). The title was originally coined for engineers embedded with external customers, and we've pointed…
Added 3 months ago
Forward Deployed Engineer, Finance
Jetson Home
North Vancouver, BC
We're building a new kind of role to help us scale: the Forward Deployed Engineer (FDE). The title was originally coined for engineers embedded with external customers, and we've pointed…
Added 3 months ago
AG
Data Scientist (ML Engineer)
Adelphi Group
Toronto, ON
- Full-time
As a Data Scientist (ML Engineer), on the Perpetua team, you will design, experiment with, and ship the machine learning systems that decide how thousands of brands spend their advertising…
Added 3 months ago
A
Data Center Specialist
Accenture
Ottawa, ON
- Full-time
You’re an experienced data center engineer who can lead complex builds, migrations, and lifecycle programs across compute, storage, and network domains. You blend deep technical skill with…
Added 2 months ago
Modern Workplace Engineer - Intune & Endpoint Management
Scotiabank
Toronto, ON · Scarborough, ON
Experience with Python, REST APIs, and cloud automation.
Added 3 months ago
CS
Staff Software Engineer, GPU Inference
Cerebras Systems
Remote, Canada
- Full-time
- Remote
Strong programming ability in C++ and Python, including experience with multithreading, concurrency, memory management, and performance-sensitive software.
Added 2 months ago
Staff Software Engineer (Platform)
Dialpad
Kitchener, ON
Our Tech Stack: Python backend on Google Cloud Platform (AppEngine, Datastore, BigQuery, Dataflow, Memcache, Cloud Tasks, Pub/Sub), Redis, Elastic Search, Monitoring (Datadog, Grafana),…
Added 3 months ago
S
Senior Data Engineer
ShyftLabs
Toronto, ON
- Full-time
ShyftLabs is seeking an experienced Senior / Lead Data Engineer to lead the design, architecture, and delivery of enterprise-scale data platforms for Fortune 500 organizations. This is a…
Added 3 months ago
A
Infra System Administrator
Accenture
Ottawa, ON
- Full-time
Engineer automation with Terraform/Ansible and CI/CD (linting, testing, policy as code) to deliver immutable infrastructure.
Added 3 months ago
A
Network Engineer
Accenture
Ottawa, ON
- Full-time
You’re a network engineer who designs secure, high‑availability networks and leads complex incident response. You combine deep platform expertise with automation to deliver predictable…
Added 3 months ago
A
Data Center Specialist
Accenture
Ottawa, ON
- Full-time
You’re an experienced data center engineer who can lead complex builds, migrations, and lifecycle programs across compute, storage, and network domains. You blend deep technical skill with…

Cerebras Systems

Staff Software Engineer, GPU Inference

Remote, Canada

Remote

Full-time

Apply About employer

Applications open in a new tab.

Job details

Job type: Full-time
Workplace: Remote
Experience: 8+ years of software engineering experience, including substantial individual-contributor ownership of complex production systems.
Posting language: English
Posted: Jul 28, 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

8+ 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 About employer

More about this job

Responsibilities

You will design, build, and maintain the GPU inference stack, ensuring high performance and reliability for large-scale AI workloads. Additionally, you will establish operational practices for the GPU fleet, including deployment, monitoring, and performance tuning across distributed systems.

Requirements

Candidates must have 8+ years of software engineering experience with a strong background in C++, Python, and production-grade GPU inference systems. A bachelor's degree in a technical field or equivalent practical experience is required, along with deep expertise in performance optimization and distributed systems.

Skills

C++
Python
GPU Inference
vLLM
PyTorch
ROCm
Distributed Systems
Performance Optimization
Kubernetes
Linux
Machine Learning Systems
Latency Optimization
Debugging
CI/CD
Observability
Parallelism

Visa sponsorship

Not detected in the job text

Keywords

GPU Inference
C++
Python
vLLM
PyTorch
ROCm
Distributed Systems
Kubernetes
Linux
Performance Profiling
Latency
Throughput
Tensor Parallelism
KV-cache
Quantization
HIP
CI/CD
Observability
AI Infrastructure
Large Language Models
Multimodal Models
GEMM
Attention Kernels
RDMA
Firmware
Driver Development
Software Engineering
Scalability
Numerical Correctness
Benchmarking

Report this job