Vancouver, Bc Java application developer Jobs – 105 Openings This Week

ML Kernel Performance Engineer, Edge AI and Science
Amazon Development Centre Canada ULC —Vancouver, BC
- $114,800–$191,800 a year
- Full-time
- Computer Science
- Java
- AWS
- Profit sharing
- Dental care
8d
Senior Curam Developer
eSystems Inc. —Vancouver, BC
- Computer Science
- Engineering
- Java
Senior Software Engineer (Full Stack)
Press Ganey —Vancouver, BC
- $110,000–$140,000 a year
- Full-time
- Azure
- Computer Science
- Java
Quick apply
Senior Guidewire Cloud Developer
BCAA —Burnaby, BC
- $100,306.20–$125,382.84 a year
- Full-time
- Hybrid work
- Computer Science
- Java
- Analysis skills
- Paid time off
Senior Software Development Engineer in Test
PDF Solutions —Vancouver, BC
- $80,000–$110,000 a year
- Full-time
- Computer Science
- CAD
- DevOps
8d
Manager, Software Development Engineering - AI Platform
Workday —Vancouver, BC
- $140,000–$210,000 a year
- Full-time
- Java
- In-person
- Communication skills
Development Manager – AI/ML Solutions
Fortinet —Burnaby, BC
- $110,000–$150,000 a year
- Full-time
- Computer Science
- Data analysis skills
- Java
- Disability insurance
- Dental care
Lead Engineer (API Platform)
Xero —Vancouver, BC
- $173,900–$217,400 a year
- Full-time
- Hybrid work
- Java
- APIs
- In-person
Senior Software Engineer
Microsoft —Vancouver, BC
- $114,400–$203,900 a year
- Full-time
- Azure
- Computer Science
- Java
Manager, Software Engineering
Infoblox —Burnaby, BC
- $135,600–$185,800 a year
- Full-time
- Hybrid work
- Computer Science
- Engineering
- Java
- Paid time off
Lead DevOps Engineer
PDF Solutions —Vancouver, BC
- $110,000–$130,000 a year
- Full-time
- Remote
- Computer science
- Azure
- Computer Science
10d
Senior Software Development Engineer, Rendering Foundations - ProductUIService
Amazon Development Centre Canada ULC —Vancouver, BC
- $150,700–$251,700 a year
- Full-time
- Computer Science
- Java
- Bachelor's
- Profit sharing
- Dental care
14d
Senior Manager, Software Engineering
Infoblox —Burnaby, BC
- $158,600–$241,560 a year
- Full-time
- Hybrid work
- Computer Science
- Java
- Master's degree
- Paid time off
Senior Pipeline TD
ICON Creative Studio —Vancouver, BC
- $80,000–$95,000 a year
- Full-time
- Computer Science
- Java
- Databases
Quick apply
Senior Engineering Manager - Android Connectivity
Rivian and Volkswagen Group Technologies —Vancouver, BC
- $174,200–$217,800 a year
- Full-time
- Hybrid work
- Computer Science
- Java
- Master's degree
14d
Senior Solutions Architect, Enterprise - Retail, CPG and Travel & Hospitality
Amazon Web Services Canada, Inc. —Vancouver, BC
- $146,000–$211,600 a year
- Full-time
- Azure
- Java
- AWS
- Profit sharing
- Dental care
Salesforce Technical Manager
Pwc Canada —Vancouver, BC
- $112,400–$162,400 a year
- Full-time
- Azure
- Salesforce
- DevOps
Principal Software Engineer - Platform, Identity and BI
Calabrio —Vancouver, BC
- On call
- Remote
- DevOps
- Java
- SQL
- Designated paid holidays
- Disability insurance
Quick apply
Apache Camel Developer - Contract 6 months - Canada (Remote)
CorGTA —Canada
- Up to $100 an hour
- Contract
- Remote
- Java
9d

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ML Kernel Performance Engineer, Edge AI and Science

Amazon Development Centre Canada ULC -
Vancouver, BC

Apply Now

Job details

Full-time
$114,800–$191,800 a year
8 days ago

Qualifications

TensorFlow
Torch
Computer Science
PyTorch
Java
OS Kernels
AWS
C++
Bachelor's
Computer networking
Software development
AI
Python
SDLC

Full job description

DESCRIPTION

Amazon Devices is an inventive research and development company that designs and engineers high-profile consumer products like the Kindle family, Fire Tablets, Fire TV, Health & Wellness devices, Amazon Echo, and Astro. We are building the next generation of edge AI capabilities through our advanced compression platform and custom neural accelerator silicon.

Within Edge AI & Science, the AI Platform team builds a compression platform—the first of its kind—enabling 20-100x neural network compression for edge and cloud deployment. As model sizes grow from billions to hundreds of billions of parameters, compute efficiency becomes the single largest return on engineering investment during training. The gap between eager-mode Python and optimized GPU execution is where months of training time are won or lost.

We are looking for an ML Kernel Performance Engineer to work at the hardware-software boundary of this platform, crafting high-performance CUDA and Triton kernels that make our compression algorithms run at peak efficiency during training, fine-tuning, and inference. You will build the tooling and kernel libraries that democratize GPU performance optimization across the team, enabling scientists and engineers to profile, diagnose, and fix kernel bottlenecks without needing to be CUDA experts themselves.

Working alongside compression scientists and platform engineers, you will ensure that novel quantization schemes (ternary, nonary, mixed-precision) and sparse computation patterns translate into real throughput gains on GPU hardware. Your work will directly accelerate every training run in the organization and unlock deployment of compressed models to both edge devices and cloud inference.

Key job responsibilities
Design and implement high-performance CUDA and Triton kernels for quantization-aware training, sparse matrix operations, and low-bit inference on modern GPU accelerators

Analyze and optimize kernel-level performance for compression training workloads, conducting detailed performance analysis using profiling tools to identify and resolve bottlenecks that slow model training from days to weeks

Implement kernel-level optimizations such as operator fusion, tiling, memory access pattern optimization, and scheduling for compression-specific compute patterns

Build a kernel development harness that enables any team member to profile kernel performance, test forward/backward accuracy, and validate at production scale, lowering the bar from "CUDA expert" to "any engineer with agents"

Maintain and extend the team's training kernels library with clean interfaces, CI, and examples that enable scientists to contribute kernel improvements alongside platform engineers

Collaborate closely with Applied Scientists, compiler engineers, and hardware architects to co-design ML-centric solutions that unify software and hardware for both cloud and edge deployment

Develop inference kernels for cloud deployment (custom backends for quantized models that keep weights packed in memory and reconstruct on the fly for compute)

Build and maintain performance regression tests and benchmarking infrastructure that track kernel efficiency as models scale from billions to hundreds of billions of parameters

A day in the life
A scientist files a ticket: "QAT training on our large model is 4x slower than expected." You pull up the profiler, identify that a custom quantizer kernel is thrashing shared memory at scale, write a Triton replacement that tiles correctly for the layer shapes at that model size, validate accuracy in the test harness, and push it to the kernels repo. By end of day, the training run that was taking four days now takes one.

You will also build the tooling that makes this workflow repeatable by others. You will participate in design discussions with Applied Scientists, translate their algorithmic ideas into efficient GPU implementations, and work in a startup-like environment where every engineering hour directly accelerates the team's ability to ship compressed models.

About the team
The AI Platform team builds Amazon's neural network compression platform. We compress models using knowledge distillation, network restructuring, and advanced quantization to achieve 20-100x compression while preserving model quality. Our platform packages these into automated pipelines that deploy to both custom edge silicon and GPU-based cloud inference.

As model sizes grow, the proprietary advantage shifts from the science to the software (making it work at hundreds of billions of parameters is the moat). GPU kernel performance is the biggest single lever on training throughput, and we expect AI-assisted development tooling to significantly multiply engineering productivity, meaning a small team with the right harness can operate at the scale of a much larger one.

The ML Kernel Performance Engineer bridges science and platforms: you turn algorithmic innovations into production-grade GPU code that runs at scale. You will work alongside Applied Scientists, compiler engineers, hardware architects, and platform developers in a small, agile team building the next generation of edge AI for Amazon's consumer products.

BASIC QUALIFICATIONS

3+ years of non-internship professional software development experience
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience with CUDA kernels or ML/low-level kernels, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
Experience with programming languages such as Python, Java, C++

PREFERRED QUALIFICATIONS

Bachelor's degree in computer science or equivalent
3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Experience with GPU kernel optimization and GPGPU computing (CUDA, Triton, SYCL, or ROCm)
Proficiency in low-level performance optimization for GPUs
Understanding of GPU memory hierarchies and optimization strategies (shared memory, L1/L2 cache, register pressure, memory coalescing)
Experience developing high-performance libraries for ML or HPC applications
Knowledge of ML frameworks (PyTorch, TensorFlow) and their GPU backends
Experience implementing custom PyTorch operators (torch.autograd.Function, C++ extensions)
Experience with parallel programming and optimization techniques
Background in neural network compression (quantization, pruning, knowledge distillation, low-rank factorization)
Knowledge of mixed-precision training and inference (FP16, BF16, FP8, INT8, INT4)
Experience with inference optimization (TensorRT, ONNX Runtime, vLLM, or similar)
Familiarity with Transformer architectures, attention mechanisms, and their compute/memory profiles
Experience with AWS Trainium/Inferentia or the Neuron Kernel Interface (NKI)
Experience with edge deployment, model compilation, or hardware-aware optimization

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.

CAN, BC, Vancouver - 114,800.00 - 191,800.00 CAD annually

Apply Now

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java application developer jobs in vancouver, bc

ML Kernel Performance Engineer, Edge AI and Science

Senior Curam Developer

Senior Software Engineer (Full Stack)

Senior Guidewire Cloud Developer

Senior Software Development Engineer in Test

Manager, Software Development Engineering - AI Platform

Development Manager – AI/ML Solutions

Lead Engineer (API Platform)

Senior Software Engineer

Manager, Software Engineering

Lead DevOps Engineer

Senior Software Development Engineer, Rendering Foundations - ProductUIService

Senior Manager, Software Engineering

Senior Pipeline TD

Senior Engineering Manager - Android Connectivity

Senior Solutions Architect, Enterprise - Retail, CPG and Travel & Hospitality

Salesforce Technical Manager

Principal Software Engineer - Platform, Identity and BI

Apache Camel Developer - Contract 6 months - Canada (Remote)

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