Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Building and operating production ML and AI systems for document understanding and reasoning in the construction industry with an accent on retrieval, extraction, and evaluation. Focus on turning ambiguous customer problems into testable technical plans and ensuring reliable intelligence for consequential work.

Location: In-person in Toronto, Canada

Salary: $170K - $250K CAD

Company

hirify.global helps construction teams understand complex project documents, identify risk, and turn fragmented information into decisions using an AI-native product.

What you will do

• Build and operate production ML and AI systems for document understanding, retrieval, extraction, and reasoning.
• Design evaluation datasets, metrics, experiments, and error-analysis loops to make quality measurable.
• Improve data pipelines and feedback loops while protecting the integrity of training and evaluation data.
• Make pragmatic decisions across prompting, retrieval, model selection, and fine-tuning.
• Build observability for model behavior, cost, latency, reliability, and regressions.
• Collaborate with product and construction experts to translate customer problems into technical plans.

Requirements

• Experience shipping and operating ML or AI systems used by real customers.
• Strong Python and software-engineering fundamentals, including testing, debugging, and APIs.
• Practical experience with modern language or multimodal models, retrieval systems, and evaluation methods.
• Sound judgment regarding data quality, experimentation, and failure modes of model outputs.
• Ability to explain technical tradeoffs clearly to product and domain partners.
• Willingness to work with the team in person in Toronto.

Culture & Benefits

• Equity package (0.05% - 0.15%).
• High level of ownership and pace in an early-stage environment.
• Focus on building reliable, evidence-based AI rather than simple demos.

Hiring process

• Introductory call (20 minutes) to discuss experience and mutual fit.
• Online technical interview (2 hours) covering ML problem framing, evaluation, and system design.
• In-office interview day featuring a whiteboarding session on production ML systems.
• References and offer.