Machine Learning Engineer (AI Platform)

TL;DR

Machine Learning Engineer (AI Platform): Building and optimizing agentic AI, search systems, and semantic parsing tools for an enterprise platform with an accent on reasoning, planning, and swarm agents. Focus on bridging the gap between deep research and production through MLOps, RAG architectures, and scalable evaluation frameworks.

Location: Hybrid (Must spend at least 50% of time per quarter in office). Primary location: Vancouver, Canada; also open to US locations.

Salary: $171,600 – $257,400 CAD (Vancouver) / $163,000 – $288,000 USD (US)

Company

hirify.global is a Fortune 500 company providing a leading AI platform for managing people, money, and agents.

What you will do

• Architect Agentic AI, including reasoning, planning, and swarm agents that interact with enterprise data.
• Develop algorithms for automated node-level optimization within agent graphs and LLM configurations.
• Build hybrid agentic search systems and semantic parsing products (Text-to-SQL/Python).
• Engineer cloud-based pipelines (Kubeflow) and A/B testing frameworks for evaluation and safety monitoring.
• Own the end-to-end MLOps process, from prompt engineering to scalable production deployment.
• Collaborate with PMs and engineers to define strategic roadmaps for "AI-first" products.

Requirements

• Must be based in Canada (Vancouver) or the USA.
• 3-6+ years of experience in production-grade ML systems (Deep Learning, NLP, IR) using PyTorch or TensorFlow.
• Experience building LLM-powered products, RAG architectures, and agentic frameworks (e.g., LangChain/LangGraph).
• Expert-level Python proficiency focusing on modular design and asynchronous patterns.
• Advanced degree (Master’s or Ph.D.) in a quantitative field or a strong research portfolio.
• Proficiency in large-scale data processing using PySpark and SQL.

Nice to have

• Experience with DSPy, Reinforcement Learning, imitation learning, or Graph Neural Networks (GNNs).
• Hands-on experience with PEFT fine-tuning and evaluation frameworks like DeepEval or RAGAS.
• Knowledge of Knowledge Graphs and "Golden Dataset" curation for model benchmarking.

Culture & Benefits

• Flex Work policy: freedom to create a flexible schedule provided 50% of time is spent in-office or in the field per quarter.
• Competitive compensation package including base salary, bonus plan, and annual refresh stock grants.
• Inclusive culture rooted in integrity, empathy, and shared enthusiasm.
• Strong investment in professional growth with tools and support for long-term development.