{"id":1026159,"url":"https://alion.io/job/evismart-aiml-engineer","title":"AI/ML Engineer","company":{"id":673288,"name":"EviSmart","domain":"evismart.com","url":"https://alion.io/company/evismart","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":95,"open_postings":6,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":148,"computed_at":"2026-10-21T05:46:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Vancouver, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":83000,"max_usd":176000,"period":"year","method":"role_country_seniority_unknown","sample_n":206},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"LLM Evaluation","optional":false},{"name":"Machine Learning","optional":false}],"status":"live","first_seen_at":"2026-09-17T23:34:52Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-21T17:40:24Z","board_verified":true,"closed_at":null,"days_open":33,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":33},"description":"On-site | Vancouver Office - 564 Beatty St\nAbout EviSmart\nEviSmart builds the automation layer that dental laboratories run on.\nA 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.\nWe 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.\nThe 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.\nWhat you'll do\nEstablish 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.\nBuild 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.\nStandardize the squads. One deployment path, one evaluation harness, one model and dataset registry across restoration design, scan quality, prescription extraction and agents.\nSolve 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.\nTake inference to production scale. Move workloads from on-premise to cloud with understood cost per inference and real concurrency headroom.\n• 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.\n• • • Before you apply - how we work\n 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.\nWe'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.\nIf you're looking for remote or hybrid, this isn't the right role and we'd rather not waste your time.\nWhat we're looking forRequired\n\nSignificant experience building and operating machine learning platforms in production - model registries, versioned datasets, CI for models, monitoring, rollback. Not research infrastructure.\nDepth in document and information extraction from unstructured, inconsistent text. Prescriptions are the core data object here and they are messy.\nYou have built a system where user corrections became better models, and you can walk through exactly how the data moved.\nGenuine evaluation rigour - you can define what \"accurate\" means for a given category, defend it, and ship against it.\nJudgment 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.\nAbility to align engineers across squads and time zones without formal reporting authority.\nStrongly preferred\nMulti-tenant or per-customer model behaviour at scale.\n3D or geometry-based ML - directly relevant to scan quality and restoration design.\nExperience with healthcare or other regulated data; patient privacy and data residency are live constraints for us.\nInference cost management and on-premise to cloud migration.\nNot required\nDental 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.\nWhat success looks like\nBy 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.\nBy six months - customer corrections flow into training data automatically. At least one production model has measurably improved through that loop.\nBy 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.\nWhy this role\nLarger competitors have bigger AI teams and a five-year head start on modelling. We are not trying to beat them at that.\nWe 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.\nIf 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.","description_format":"text","description_chars":5290,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Dental Products & Labs","Practice Management Software"],"lifecycle":[{"event":"open","at":"2026-09-18T11:30:14Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.902,"p_room":1,"age_days":33,"expected_fill_days":148,"reasons":["conf:2","velocity","win:early"],"computed_at":"2026-10-21T05:46:15Z"},"pay":null,"html_url":"https://alion.io/job/evismart-aiml-engineer","json_url":"https://alion.io/job/evismart-aiml-engineer.json","meta":{"generated_at":"2026-10-21T19:50:20Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":28097,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-22T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":673288},"rest":"https://alion.io/mcp/rest/get_company?id=673288"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fevismart-aiml-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fevismart-aiml-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fevismart-aiml-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/evismart-aiml-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fevismart-aiml-engineer"}]}