Machine Learning Engineer (Hybrid)

Introduction

Provision is automating construction. We build AI-native tools that help construction teams understand complex project documents, identify risk, and turn fragmented information into decisions. We are building the most ambitious vision of what is possible with AI in construction, and we expect our engineers to be AI-forward in how they build.

The Role

This role is focused on applied ML systems across the full lifecycle. You will work with engineering, product, and domain experts to improve document understanding, evidence retrieval, answer evaluation, and how we learn from customer workflows.

What You Will Do

• Develop and maintain production ML/AI systems for document analysis, retrieval, extraction, and reasoning.
• Create evaluation datasets, metrics, experiments, and error-analysis frameworks.
• Improve data pipelines and feedback mechanisms while maintaining data integrity.
• Choose among prompting, retrieval, model selection, fine-tuning, and traditional ML techniques.
• Implement monitoring for performance, cost, latency, reliability, and regressions.
• Partner with product and construction domain experts on technical strategy.
• Write production-grade Python code across the application stack.

What We Are Looking For

• Proven track record shipping and operating ML/AI systems with real customers.
• Strong Python fundamentals with software engineering expertise (testing, debugging, APIs, data systems).
• Hands‑on experience with modern language models, multimodal systems, retrieval, and evaluation.
• Sound judgment regarding data quality, experimentation, failure modes, and model limitations.
• Ability to communicate technical tradeoffs and partner effectively across teams.
• Comfort with ownership, speed, and uncertainty in an early‑stage setting.
• Availability for in‑person work in Toronto.

Interview Process

Introductory call (20 minutes): Provision, role fit, and your background.

Online technical interview (2 hours): a collaborative ML problem covering framing, evaluation, data, system design, and production considerations.

In‑office interview day: team meetings and whiteboarding on production ML system design.

References and offer upon mutual fit.

Compensation

• $170K - $250K CAD
• Equity: 0.05% - 0.15%

Provision does not use AI for applicant screening or assessment. Accommodations are available throughout the recruitment process.