Experience with LLMs and understanding of their functionality., Proficient in Python and backend frameworks like FastAPI or Flask., Strong knowledge of Natural Language Processing (NLP) techniques., Familiarity with CI/CD processes and cloud services such as AWS, GCP, or Azure..
Key responsabilities:
Build and improve AI pipelines by writing and deploying production Python code.
Experiment with LLM tuning strategies to identify project blockers and risks.
Own the development of features from prototype to production-ready deployment.
Research and implement new tools and techniques to enhance AI capabilities.
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Axiom Zen is a venture studio. We turn ideas into companies.
Our specialty is testing the potential of emerging technology to build high-growth businesses. Today that means we're working on AI, AR, and blockchain, either independently or in partnership with Fortune 500 leaders and domain-expert investors.
Axiom Zen was named first among Canada's Most Innovative Companies by Canadian Business. Our work has been featured in TIME Magazine, The New York Times, and Fast Company.
Products developed by Axiom Zen have touched 200+ million consumers and are used by the world’s leading organizations. Recent successes include NBA Top Shot, and CryptoKitties, products created by our startup Dapper Labs. Axiom Zen is also the team behind ZenHub, the world’s leading collaboration solution for technical teams; and the creator of Toby, tab management with personality recognized as Top Chrome Extension of the year by both Google and Product Hunt.
We are founded on Axioms—philosophies that drive our company culture and partnerships.
Work with the best
Do the best work
Make a difference
Be sustainable
We’re hiring. Let’s build the future together: https://www.axiomzen.co/careers
We’re looking for a curious and inventive AI Systems Engineer to help us push the limits of what AI can do for engineering teams. You’ll be joining a small, fast-moving team where ideas turn into shipped features quickly, and where your work will have an immediate impact. You’re someone who loves solving cutting-edge AI challenges, but also knows how to get things into production. You’ll be hands-on with everything from building core application services to tinkering with LLM internals.
About us:
We’re building AI-first tools to make engineering teams faster and smarter. Our mission is to save developers from busywork and give them time to focus on what they’re actually passionate about — building great products & services. From AI-powered summaries and insight generation to spotting blockers before they derail projects, we’re using LLMs and automation to take the friction out of dev workflows.
We’re still early-stage with our AI-powered products and features, so there’s a lot of room to shape where this goes—and a lot of interesting problems left to solve.
What You'll Do
Build and improve dynamic AI pipelines by designing, writing & deploying production Python code
Experiment with various LLM tuning strategies to answer complex qualitative questions (e.g. can AI help identify which tasks are blockers or risks)
Boost the quality of AI-generated outputs—whether it’s improving summaries, surfacing insights, or generating new categories from scratch
Own end-to-end features: from scrappy prototype to stable, production-ready deployment
Configure, maintain and deploy distributed application services to cloud environments
Get your hands dirty across backend, infrastructure, and AI/ML workflows
Iterate fast: tweak prompts, tune models, test outputs, and constantly improve
Research new tools, techniques and frameworks to keep us ahead of the curve
About You:
You’re AI-savvy, you’ve worked with LLMs and understand how they function under the hood
You have a builder mindset. You’ve shipped real Python code to production in a team environment and are comfortable with backend frameworks (FastAPI, Flask, etc.)
You know how to engineer LLM prompts, validate outputs, and iterate quickly to get high quality results
You have experience using Natural Language Processing (NLP) techniques to extract, transform, and parse textual data into meaningful representations suitable for downstream LLM-based operations.
You’re used to experimenting and prototyping in Notebooks
You have strong DevOps fundamentals, and experience with CI/CD & cloud services (AWS, GCP, Azure)
You have experience with monitoring tooling and troubleshooting production issues
You’re self-directed, adaptable, and love wearing multiple hats—R&D one day, demoing & debugging your latest pipelines with customers the next
You can communicate complex technical concepts to non-technical folks (we may ask you to explain how an LLM works under-the-hood)
You care about how your work impacts users and drives business value
You’re always testing new tools or reading up on the latest AI trends
Bonus Points
Experience with TensorFlow, PyTorch, or deploying open-source LLMs (Llama, Mistral, etc.) on your own infra
Knowledge of graph databases or vector databases
Hands-on with serverless (AWS Lambda) or cloud-native tooling (Kubernetes, Docker)
An academic or practical background in ML and/or Natural Language Processing (NLP) or computer science
Ideally based in Vancouver (but we’re open to remote across the Americas)
Perks:
Flexible remote work + unlimited vacation (we actually take it)
Annual learning & development budget (conferences, books, courses)
Health & wellness perks
Top-tier gear—whatever you need to do your best work
A no-ego, collaborative team that’s serious about building something great
How to apply:
Send us your resume and/or LinkedIn, plus a link to a project, repo, or anything you’re proud of!
Required profile
Experience
Spoken language(s):
English
Check out the description to know which languages are mandatory.