Principal Machine Learning Engineer (AI)

Location: Remote role associated with Seattle, Mountain View, and San Francisco, United States. hirify.global can hire in countries where it has a legal entity.

Salary: United States base pay ranges from $196,461 to $309,025 annually, depending on geographic pay zone.

Company

hirify.global develops collaboration software and the Teamwork Graph, a real-time permissions-aware knowledge graph connecting people, teams, projects, content, and activities.

What you will do

• Build personal work context graphs and graph inference pipelines from connected tools.
• Define graph schemas, permissions, and evaluation frameworks for reliable inferred context.
• Integrate graph-derived context into Rovo Chat to improve relevance, groundedness, and efficiency.
• Build low-latency, permission-safe APIs and CLI experiences, including MCP-compatible agent access.
• Lead technical direction across Knowledge AI, Teamwork Graph, and product teams while mentoring engineers.
• Champion responsible, privacy-safe AI and high-quality data practices.

Requirements

• 8+ years of experience in machine learning or AI engineering.
• Deep expertise in knowledge graphs, graph neural networks, or entity and relationship extraction.
• Experience shipping production-scale ML-powered graph inference or knowledge representation systems.
• Hands-on experience with graph databases, graph query languages, or large-scale graph processing frameworks.
• Experience delivering end-to-end ML features from data pipelines and model training through serving, monitoring, and iteration.
• Strong understanding of LLM orchestration, retrieval-augmented generation, context injection, and ML evaluation methods.

Nice to have

• Experience with enterprise knowledge graphs, semantic embeddings, or ontology design at scale.
• Familiarity with permission-aware data systems and privacy-by-design principles.
• Background in collaboration analytics, social network analysis, or user activity modeling.
• Master's or PhD in Computer Science, Machine Learning, Information Retrieval, or a related field.

Culture & Benefits

• Choice of working from an office, from home, or through a combination of both.
• Health and wellbeing resources.
• Paid volunteer days.
• Potential eligibility for benefits, bonuses, commissions, and equity.
• Support for accommodations or adjustments during the recruitment process.