AI/ML Engineer
At a glance
Mid-level Machine Learning Engineering role at Evismart. Vancouver · no salary stated · we estimate CA$128,063–CA$187,710 across Canada.
Evismart has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Create accuracy views by model, customer and category so product and sales teams can see where performance needs work
- Turn customer quality corrections into versioned training examples, with people reviewing cases the system is unsure about
- Give four AI squads shared deployment, evaluation, model and dataset practices across their different workloads
- Improve behavior across customer-specific codes and prescription preferences without making a separate model for each lab
- Shift inference from on-premise systems to cloud while tracking per-inference cost and capacity under concurrent use
What you bring
- Substantial experience operating production ML platforms, including model versioning, data management, monitoring and rollback
- Deep experience extracting information from inconsistent documents, plus a clear account of how user corrections improved a model
- Strong evaluation judgment: define and defend accuracy measures, and know when rules work better than ML
- Ability to coordinate engineers across squads and time zones without managing them directly
Who this fits
This role suits a hands-on technical lead who can set AI direction and align existing squads without formal authority. You will work with the CTO, Product and Manila operations teams. The position is full-time and in-office at the Vancouver office five days a week; remote and hybrid arrangements are not available.
From the employer
On-site | Vancouver Office – 564 Beatty St
About EviSmart
EviSmart builds the automation layer that dental laboratories run on.
A dental lab is a manufacturing operation. A case arrives from a dentist — a 3D scan of a patient's mouth and a prescription — and leaves as a crown, bridge, denture or night guard. In between sit a dozen manual steps: monitoring scanner portals, retyping cases into lab software, checking scan quality, interpreting handwritten prescriptions, designing the restoration.
We automate that pipeline. Our software connects to 15 intraoral scanner portals and more than five lab management systems, pulls cases the moment they arrive, reads the prescription, maps it into the lab's own product catalogue, checks the scan, and routes it to design. We serve roughly 6,000 customers across 28 countries from offices in Vancouver, Manila, Seoul and Shenzhen.
The automation works. It does not yet work well enough, consistently enough, to run unattended — and closing that gap is the highest-leverage technical problem in the company.
What you'll do
- Establish measurement. Build per-model, per-customer, per-category accuracy reporting that Product and Sales can act on. Nothing else you do matters until this exists.
- Build the correction flywheel. Capture the quality-control corrections our customers already make, turn them into structured, versioned training data, and close the loop with human review where confidence is low.
- Standardize the squads. One deployment path, one evaluation harness, one model and dataset registry across restoration design, scan quality, prescription extraction and agents.
- Solve multi-tenant model behaviour. Thousands of customer-specific product codes, prescription conventions and unwritten preferences sitting over a shared model base — without building a bespoke model per customer.
- Take inference to production scale. Move workloads from on-premise to cloud with understood cost per inference and real concurrency headroom.
• • Set technical direction for AI across the company, partnering closely with the CTO, Product and the operations teams in Manila who currently absorb the work our models can't yet do.
• • • • Before you apply — how we work
This is a full-time in-office role at our Vancouver office, five days a week. We are not offering remote or hybrid arrangements for this position, and this is not negotiable at offer stage.
We're explicit about it because the work genuinely depends on it. You'll be standardizing how four existing AI squads build, working across a Manila engineering organization, and spending real time with the operations people who currently absorb the work our models can't yet do. That happens in a room.
If you're looking for remote or hybrid, this isn't the right role and we'd rather not waste your time.
- What we're looking for
Required - Significant experience building and operating machine learning platforms in production — model registries, versioned datasets, CI for models, monitoring, rollback. Not research infrastructure.
- Depth in document and information extraction from unstructured, inconsistent text. Prescriptions are the core data object here and they are messy.
- You have built a system where user corrections became better models, and you can walk through exactly how the data moved.
- Genuine evaluation rigour — you can define what "accurate" means for a given category, defend it, and ship against it.
- Judgment about when not to use a model. A significant share of this problem is classification and mapping, and deterministic rules beat ML for a lot of it.
- Ability to align engineers across squads and time zones without formal reporting authority.
- Strongly preferred
- Multi-tenant or per-customer model behaviour at scale.
- 3D or geometry-based ML — directly relevant to scan quality and restoration design.
- Experience with healthcare or other regulated data; patient privacy and data residency are live constraints for us.
- Inference cost management and on-premise to cloud migration.
• • Not required
Dental or medical-device background. The workflow is learnable in an afternoon and we would rather have platform depth. What we do want is curiosity about a strange, physical, high-stakes manufacturing process — the output of our software ends up in someone's mouth.
What success looks like
By 90 days — you can tell us, with evidence, where our models actually fail and for which customers. The squads agree on a single evaluation standard.
By six months — customer corrections flow into training data automatically. At least one production model has measurably improved through that loop.
By twelve months — prescription quality control runs with confidence thresholds, routing only genuinely ambiguous cases to human review. We can state our accuracy publicly and defend it in a sales conversation.
Why this role
Larger competitors have bigger AI teams and a five-year head start on modelling. We are not trying to beat them at that.
We are trying to build something they structurally cannot: a system that gets better every time one of six thousand laboratories tells us we got something wrong. That data exists. The loop does not.
If you have built this kind of flywheel before — or you have been close enough to one to know exactly why most of them fail — we should talk.