Montreal, QC
ML engineering recruiting for production model teams. Crosscheck recruits AI/ML & LLM Engineering candidates for contract, contract-to-hire, and permanent roles tied to Montreal.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Montreal business context. Montreal's 2030 Economic Plan separates digital intelligence and creativity, life sciences, and advanced manufacturing and materials. The plan also connects aerospace and clean technology with the city's manufacturing strategy. A Machine Learning Engineer search needs a defined prediction task, training data owner, deployment path, and measure of useful performance. The same title can describe notebook research, feature engineering, backend development, or ownership of an inference service.
Screen ML engineers on modeling fundamentals and production tradeoffs
Match experience to the ML stack, data, and problem defined in the brief
target a first candidate slate within 48 hours for qualified exclusive searches in our core disciplines after a completed intake
Permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
Start the search
Include the business context, systems, delivery phase, work model, compensation, and interview timeline. A Crosscheck search lead will use that context to calibrate the role before sourcing begins.
Local Market Brief
Write the brief around the model lifecycle. Include label creation, feature pipelines, experiment tracking, service integration, monitoring, and retraining duties that belong to this hire. Separate those duties from work owned by data, platform, or research teams. The three sourced Montreal contexts below turn that scope into intake and screening decisions. They do not measure current vacancies, candidate supply, or Crosscheck client activity.
Editorial market scenario
Ask candidates to show how they moved technical work into a maintained system. Record the handoff, monitoring, documentation, and operating constraints. This is planning guidance, not measured local demand.
Editorial industry scenario
A research-facing brief should separate experimental work from ownership of maintained systems, users, documentation, and deadlines. Confirm that this context applies to the employer before using it in the search.
Screening focus
We test for model or application ownership, evaluation discipline, data judgment, and evidence that the candidate has shipped reliable AI systems.
Published Canadian regional profile
These values describe the Montréal census metropolitan area in the 2021 Census. They are dated regional context, not a current count of Montreal-area technology candidates, vacancies, clients, or Crosscheck placements.
Statistics Canada 2021 Census Profile, released December 15, 2022. Geography ID 2021S0503462.
Natural and applied sciences and related occupations
217,730; 9.4%
Statistics Canada's 2021 Census Profile reports 217,730 and a 9.4% published rate for natural and applied sciences and related occupations in the Montréal census metropolitan area. This broad occupational group includes many jobs outside the specialty on this page and does not measure candidate availability.
Mathematics, computer and information sciences
115,005; 3.3%
Statistics Canada's 2021 Census Profile reports 115,005 and a 3.3% published rate for mathematics, computer and information sciences in the Montréal census metropolitan area. This is a field-of-study characteristic, not a current count of people working in a matching occupation.
Worked at home
545,855; 25.8%
Statistics Canada's 2021 Census Profile reports 545,855 and a 25.8% published rate for worked at home in the Montréal census metropolitan area. This 2021 reference-period measure is historical context, not a current remote-work forecast.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Montreal demand, clients, or candidate supply.
Sourced digital intelligence and creativity context
Montreal's 2030 Economic Plan identifies artificial intelligence and data science, cybersecurity, digital creativity, and virtualization as strategic digital niches. Tie the sector scenario to a concrete outcome and data-generating process. Ask how the engineer would detect label leakage, sampling bias, missing history, and a metric that looks strong but fails the business use case. Digital work can combine models, source data, identity, cloud services, media assets, rights, user analytics, releases, threat response, and production support.
Evidence to request: Review a feature or training pipeline and ask the candidate to trace one record from source through validation, training, and evaluation. Define the user and product, model or service boundary, data rights, identity controls, evaluation or release method, threat response, operating target, and approval owner.
Sourced life sciences context
The Montreal plan names life sciences as a recognized key sector and includes biomedical work in its advanced manufacturing and materials priorities. Define the inference environment before screening. Batch scoring, low-latency APIs, edge deployment, and analyst-facing tools require different software design and operating evidence. Life-sciences delivery may span experiments, laboratories, clinical records, devices, quality systems, regulated manufacturing, protected data, and commercial operations.
Evidence to request: Use a production scenario with throughput, latency, and reliability limits. Require an API or batch design plus a test plan. Name the research or product stage, regulated boundary, source record, validation protocol, device or laboratory interface, access controls, release authority, and reviewer.
Sourced advanced manufacturing, aerospace, and clean technology context
Montreal's economic plan identifies advanced manufacturing and materials, aerospace, aviation, clean technology, energy, construction, and transportation among its strategic sectors and niches. Assign responsibility for drift, retraining, and model retirement. Candidates should explain the thresholds, review cadence, and human decision that follows an alert. These programs can join engineering changes, materials, plants, assets, suppliers, quality, maintenance, energy measures, emissions, transport, contracts, and financial records.
Evidence to request: Ask for a model degradation example and the evidence used to separate data change, code change, and user-behavior change. Set the product and facility boundary, configuration baseline, production model, traceability, quality release, asset interfaces, energy calculations, change window, and acceptance evidence.
Interview scorecard
Ask each candidate the same core questions. Score the evidence, ownership, and judgment in the answer instead of relying on job-title or keyword matches.
1. ML Engineer: PyTorch
Choose a PyTorch decision from your work as ML Engineer. Which constraint changed the design, and what evidence supported the result?
Use the answer to assess experiment design, evaluation, reproducibility, deployment, monitoring, and product ownership. The research and technical commercialization context is an editorial scenario, not a measured claim about Montreal.
2. Senior ML Engineer: TensorFlow
Describe project work you completed as Senior ML Engineer involving TensorFlow that did not follow the original plan. What did you own, and how did you correct it?
Use the answer to assess evaluation tied to user tasks, latency, cost, monitoring, fallback behavior, and product ownership. The product and software delivery context is an editorial scenario, not a measured claim about Montreal.
3. Staff / Principal ML Engineer: JAX
For a JAX system you supported, explain the handoff, operating limits, and measures used after launch. Where did your responsibility begin and end?
Use the answer to assess experiment design, evaluation, reproducibility, deployment, monitoring, and product ownership. The research and technical commercialization context is an editorial scenario, not a measured claim about Montreal.
Need the full ML Engineer evaluation guide?
The role guide covers technical scope, interview questions, and evidence checks once, without repeating the same material on every city page.
FAQ
For qualified exclusive searches in our core disciplines, Crosscheck targets a first candidate slate within 48 hours after a completed intake. Contract placements include a 40-billable-hour replacement guarantee, and permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
Continue your research