Jobs in British Columbia (88,031 jobs)

Jobs across British Columbia, Canada. Choose a city or town to search nearby.

88,031 listings · 12,278 employers Counted Nov 8, 2026. How we count

About these jobs

Employers hiring include Health Match BC (4,657 listings), Vancouver Coastal Health (1,716 listings), Fraser Health (1,538 listings).

Common roles: Family physician, Registered nurse, Retail sales associate.

20,312 of these listings include employer-provided pay. Open a job to see its currency and pay period.

Advertised starting pay
Pay period	Median starting pay	Listings
CAD / hour	27.50	14,130
CAD / year	80,000.00	5,101
CAD / month	5,027.00	634
CAD / week	1,450.00	13
USD / hour	22.00	10
USD / year	115,000.00	10

Employer-listed minimums from at least 10 listings per currency and pay period. These are advertised starting amounts, not employee salaries. Jobs without a minimum and unusual amounts are excluded.

Workplace classifications: 0% remote, 11% hybrid, 89% on-site, 0% not specified. Hybrid and on-site classifications may be inferred from descriptions. Check the original ad.

Based on the dated count above. Listings can change. How we count

Jobs in British Columbia

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Added 1 day ago
SD
Front Store Supervisor, Hourly Part Time Day
Shoppers Drug Mart
Tsawwassen, BC
- Part-time
Added 1 day ago
SS
Care Manager
Sunrise Senior Living
Vancouver, BC
- Part-time
Added 1 day ago
LA
Store Manager
Lordco Auto Parts
Armstrong, BC
- $65,000 to $75,000 CAD / year
- Full-time
Added 1 day ago
PL
YVR - Barista
Paradies Lagardère
Vancouver, BC
- Part-time
Added 1 day ago
PL
YVR - PT Warehouse Associate
Paradies Lagardère
Vancouver, BC
- Part-time
Added 1 day ago
SL
apartment building maintenance worker
Skyline Living
Nanaimo, BC
- Full-time
Added 1 day ago
HM
Family Practitioner/General Practitioner
Health Match BC
Penticton, BC
- Internship
Added 1 day ago
HM
Psychiatry
Health Match BC
Penticton, BC
- Internship
Added 1 day ago
HM
Family Practitioner/General Practitioner
Health Match BC
Chilliwack, BC
- Full-time
Added 1 day ago
HM
Family Practitioner/General Practitioner
Health Match BC
Chilliwack, BC
- Full-time
Added 1 day ago
LC
Grocery Clerk Part Time Night
Loblaw Companies Limited
Coquitlam, BC
- Part-time
Added 1 day ago
LC
Personal Shopper Part Time Day
Loblaw Companies Limited
Vernon, BC
- Part-time
Added 1 day ago
LC
Cashier Part Time Day
Loblaw Companies Limited
Kelowna, BC
- Part-time
Added 1 day ago
HM
Family Practitioner/General Practitioner
Health Match BC
Kelowna, BC
- Full-time
Added 1 day ago
BL
Client Relations & Marketing Coordinator
BC Legal Management Association
Vancouver, BC
- Other
Added 1 day ago
HM
Family Practitioner/General Practitioner
Health Match BC
Penticton, BC
- Full-time
Added 1 day ago
HM
Family Practitioner/General Practitioner
Health Match BC
Penticton, BC
- Internship
Added 1 day ago
HM
Family Practitioner/General Practitioner
Health Match BC
Kelowna, BC
- Full-time
Added 1 day ago
HM
Family Practitioner/General Practitioner
Health Match BC
Kelowna, BC
- Full-time
Added 1 day ago
HM
Family Practitioner/General Practitioner
Health Match BC
Kelowna, BC
- Full-time
Added 1 day ago
HM
Obstetrics/Gynecology
Health Match BC
Penticton, BC
- Internship
Added 1 day ago
Estimator
Kirmac Collision and Autoglass
Kamloops, BC
Added 1 day ago
Casual Outreach Worker (Tricities)
Connective
Coquitlam, BC · Maple Ridge, BC · Pitt Meadows, BC · Port Coquitlam, BC
Added 1 day ago
Casual Outreach Worker (Abbotsford)
Connective
Abbotsford, BC
Added 1 day ago
Recreation Assistant (Casual) - The Waterford
Century Group
Delta, BC

Huawei Technologies Canada Co., Ltd.

Senior AI System Engineer - LLM Training System

4321 Still Creek Drive, Vancouver, BC, V5C 6S7, Canada

The total target annual compensation ranges from $127,000 to $225,000 depending on education, experience, and demonstrated expertise. · Full-time

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Applications open in a new tab.

Job details

Pay: The total target annual compensation ranges from $127,000 to $225,000 depending on education, experience, and demonstrated expertise.
Job type: Full-time
Posting language: English
Posted: Nov 3, 2026

Original job description

Huawei Canada has an immediate permanent opening for a Senior Engineer.

About the team:

The Computing Data Application Acceleration Lab aims to create a leading global data analytics platform organized into three specialized teams using innovative programming technologies. This team focuses on full-stack innovations, including software-hardware co-design and optimizing data efficiency at both the storage and runtime layers. This team also develops next-generation GPU architecture for gaming, cloud rendering, VR/AR, and Metaverse applications. One of the goals of this lab are to enhance algorithm performance and training efficiency across industries, fostering long-term competitiveness.

About the job:

As a Senior System Engineer, you will drive core compute operator development and distributed scaling performance across foundation model pre-training and post-training workloads on Ascend NPUs. You will identify computational and communication bottlenecks, implement high-performance kernels using AscendC, Triton, and TileLang, and optimize distributed acceleration suites (MindSpeed / open-source training frameworks).

Ascend High-Performance Kernel Development: Design, implement, and tune core operators (FlashAttention variants, FP8/MXFP8/W4A8 GEMM, Fused RMSNorm, dynamic MoE routing/dispatch) using AscendC, with explorations in Triton (Triton-Ascend backend) and TileLang.
Pre-training & Post-training System Optimization: Architect, scale, and optimize distributed pre-training and RL post-training pipelines using MindSpeed (MindSpeed-LLM / MindSpeed-MM) alongside upstream frameworks (Megatron-LM / Megatron-Core, PyTorch FSDP, veRL, vLLM, Ray).
Distributed Parallelism & Training Efficiency: Tune multi-dimensional hybrid parallelism (TP, PP, CP, EP, ZeRO/FSDP) and RL rollout/actor pipelining on Ascend NPU clusters, achieving optimal MFU and compute-communication overlap.
Low-Precision & Numerical Parity: Implement low-precision training/inference schemes (FP8, MXFP8, mixed-precision), guaranteeing numerical stability, batch-invariance, and strict precision parity between training and serving runtimes.
Profiling & Hardware-Aware Tuning: Conduct deep performance profiling on Ascend hardware using Ascend Profiler (msprof), msprobe, and PyTorch Profiler to eliminate pipeline bubbles, AICore/AIVector stalls, and HCCL communication overhead.
Open Source & Ecosystem Enablement: Contribute high-performance operators, bug fixes, and acceleration features to open-source Ascend communities and upstream AI acceleration projects.
The total target annual compensation ranges from $127,000 to $225,000 depending on education, experience, and demonstrated expertise.

About the ideal candidate:

Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related technical disciplines, 3+ years of experience in high-performance computing (HPC), AI accelerator operator development, or distributed large model training systems.
Hands-on Operator Programming: Solid development experience in AscendC / CANN, or extensive experience in CUDA C/C++ with a proven ability to transition to AscendC. Experience or strong interest in Triton or TileLang for accelerator backends.
Distributed Frameworks: Deep practical experience with distributed pre-training and/or post-training frameworks, such as MindSpeed, Megatron-LM / Megatron-Core, PyTorch FSDP, veRL, or vLLM / DeepSpeed. Systems Programming: Strong programming proficiency in C/C++ and modern Python, with hands-on experience building PyTorch custom C++/CANN extensions.
Performance Analysis: Proficiency in performance profiling and debugging tools on AI hardware (e.g., Ascend Profiler/msprof, PyTorch Profiler, or Nsight toolchains).
Preferred / Standout Qualifications:
Direct experience porting or optimizing Triton or TileLang frontends/backends for non-CUDA accelerators (especially Ascend NPU).
Deep understanding of Ascend NPU microarchitecture (Cube & Vector compute units, local memory hierarchy/UB, memory movement engines, and HCCL collective communication). Hands-on experience optimizing end-to-end MFU for massive MoE or dense foundation models on multi-node NPU/GPU clusters.
Experience in low-precision numerical alignment (FP8, MXFP8, W4A8/W8A8) and eliminating training-inference discrepancy. Active open-source contributions to Ascend repos (MindSpeed, CANN ops, Ascend-vLLM) or upstream projects (Megatron-LM, Triton, TileLang, veRL).

Additional Information：

Huawei Canada is committed to a fair, inclusive, and accessible recruitment process. If you require accommodation during any stage of the hiring process, please let us know and we will work with you to meet your needs.

All applications for this position are reviewed directly by our hiring team, we do not use artificial intelligence tools to screen or select candidates.

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More about this job

Responsibilities

You will drive core compute operator development and distributed scaling performance for foundation model training on Ascend NPUs. Additionally, you will architect and optimize distributed pre-training and post-training pipelines while conducting deep performance profiling to eliminate hardware bottlenecks.

Requirements

Candidates must hold a degree in a technical discipline and possess at least 3 years of experience in high-performance computing or AI accelerator development. Strong proficiency in C/C++ and experience with distributed training frameworks like Megatron-LM or PyTorch FSDP are required.

Skills

AscendC
CUDA
C++
Python
Distributed Systems
Large Language Models
Triton
TileLang
MindSpeed
Megatron-LM
PyTorch
Performance Profiling
HPC
NPU Architecture
Parallelism
Low-precision Training

Visa sponsorship

Not detected in the job text

Keywords

AscendC
CANN
CUDA
Triton
TileLang
MindSpeed
Megatron-LM
PyTorch
FSDP
vLLM
DeepSpeed
NPU
GPU
HPC
FlashAttention
MoE
FP8
MXFP8
Distributed Parallelism
Performance Profiling
msprof
HCCL
Machine Learning
Artificial Intelligence
System Engineering
Software-hardware Co-design
Foundation Models
RLHF
Numerical Stability
Compute-communication Overlap

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