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Canada Quant Labs

AI Engineer

Victoria, BC, Canada
Greater Victoria Metropolitan Area, Canada

Full-time

Apply About employer

Applications open in a new tab.

Job details

Job type: Full-time
Apply by: Nov 8, 2026
Posting language: English
Posted: Oct 9, 2026

Original job description

The short version - Canada Quant Labs is hiring an AI Engineer to own pieces of our train → quantize → deploy pipeline. You'll ship open-weight checkpoints (like our Hy3, GLM-5.2, and DeepSeek-V4 quants), build the eval harnesses that prove they hold up, and serve them through vLLM. Full-time, hybrid in Victoria, BC. CAD $140,000–$190,000 + equity.

The role

We're Canada's open-weight model lab: we train, quantize, and deploy sovereign AI on Canadian Blackwell silicon for the industries - legal, medical, defence, finance - that can't run on someone else's API. Everything we ship is public: weights, recipes, benchmarks, and the engineering log of what broke along the way.

This is a hands-on engineering role on the model team, not a research-caretaker role. The person in it trains models, breaks them, measures them, and ships them - then writes up what happened so the community doesn't have to re-walk the dead-ends.

What you'll do

Train and fine-tune open-weight models on our DGX B300 cluster - domain distills, reasoning and agentic post-training, and Canadian-corpora adaptations for legal, medical, defence, and finance buyers.
Own quantization recipes end-to-end - W4A16 and NVFP4 with llm-compressor, calibration-data selection, and quality validation against the FP8/BF16 references.
Build and run the eval harnesses that decide what ships: reasoning, instruction-following, long-context retrieval, and domain benchmarks like our CanLegal Bench, whose leaderboard is now live.
Serve what you ship through vLLM - speculative decoding (MTP), expert-parallel MoE serving, throughput/latency profiling, and the kernel-level debugging when numbers don't make sense.
Publish everything - model cards, reproducible recipes, benchmark methodology, and engineering logs - and upstream fixes to the open stack (vLLM, llm-compressor) when you find them.

What you bring

Strong PyTorch and transformer fundamentals. You've trained or fine-tuned multi-billion-parameter models and can reason about what's happening inside the network - not just drive the framework.
Real LLM serving or quantization experience. You've shipped something with vLLM, TensorRT-LLM, SGLang, or similar - or you've built quant/serving tooling (GPTQ, AWQ, NVFP4, activation-aware recipes) that other people used.
Systems instincts. Comfortable reading CUDA/perf profiles, reasoning about memory footprints and tensor/expert parallelism, and hunting a 13% throughput regression down to a kernel flag.
Evidence of craft. A checkpoint, a kernel, an eval harness, a merged PR, a write-up - something public we can look at. We hire from artifacts, not credentials.
Clear technical writing. Our work is open by default; your model cards and engineering logs are part of the product.

Nice to have

MoE training or serving experience (expert parallelism, router behaviour, sparse-attention variants).
Speculative decoding - MTP/EAGLE-style draft heads, acceptance-rate tuning.
Eval science: benchmark design, contamination hygiene, statistical rigour about run-to-run noise.
Background in a regulated domain (legal, medical, defence, finance) or with Canadian data-residency constraints.
Open-source maintainership or a meaningful contribution history in the inference/training stack.

How we work

Small team, hybrid in Victoria, BC - we like being in the room for design reviews and launch days, and we don't count hours the rest of the time. Compute is dedicated, not queued: experiments start when you're ready. Decisions get written down, results get published, and the person who did the work presents it.

Compensation & logistics

Posted per BC pay-transparency requirements. We hire for impact; exceptional candidates can exceed the range. Base salary CAD $140,000–$190,000 Equity Meaningful early-stage grant Location Victoria, BC

hybrid (relocation support available) Work authorization Canadian citizens & permanent residents preferred Start Flexible
we're hiring now

How to apply

Apply below - a short note and your résumé are all we need. Links to artifacts (Hugging Face, GitHub, papers, benchmarks, write-ups) help a lot.

We read every application and reply within two weeks. The process: intro call → technical deep-dive on something you've built → paid half-day work trial on real cluster work → offer.

How To Apply

Apply for this role

Tell us who you are, attach your résumé, and point us at the hardest model system you've made work. We reply within two weeks.

Full name Email Links - portfolio / GitHub / LinkedIn, one per line

optional Cover note - short, why CQL
optional Résumé

Drop your résumé here, or browse files PDF, DOC, DOCX, TXT or MD

max 10 MB

Company

Prefer email? careers@cql.ca

Apply About employer

More about this job

Responsibilities

Train and fine-tune open-weight models, develop and validate quantization recipes and evaluation harnesses, and serve models through vLLM while profiling performance and debugging kernels. Publish model cards, reproducible recipes, benchmarks, and engineering logs, and contribute fixes to open-source tools.

Requirements

Candidates should have strong PyTorch and transformer fundamentals, experience training or fine-tuning multi-billion-parameter models, and practical LLM serving or quantization experience. They should be comfortable with CUDA and performance analysis, have public evidence of technical work, and communicate clearly in writing.

Skills

PyTorch
Transformer Models
Model Training And Fine-Tuning
LLM Quantization
LLM Serving
vLLM
CUDA Performance Profiling
Kernel Debugging
Evaluation Harness Development
Benchmark Design
Speculative Decoding
Mixture-Of-Experts Serving
Tensor And Expert Parallelism
Technical Writing
Open-Source Contribution

Benefits

Equity
Relocation Support

Visa sponsorship

Not detected in the job text

Keywords

PyTorch
Transformers
Open-Weight Models
DGX B300
Blackwell Silicon
W4A16
NVFP4
llm-compressor
FP8
BF16
vLLM
TensorRT-LLM
SGLang
GPTQ
AWQ
CUDA
Speculative Decoding
MTP
EAGLE
Mixture Of Experts
Expert Parallelism
Long-Context Retrieval
Evaluation Harnesses
Benchmark Design
CanLegal Bench
Domain Distillation
Agentic Post-Training
Canadian Data Residency
Model Cards
Open-Source Contributions

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