Principal Machine Learning Engineer

Overview

Principal Machine Learning Engineer responsible for shaping foundational AI infrastructure for agent runtime, evaluation, quality measurement, model optimization, fine-tuning, and model routing.

What you'll do

• Build infrastructure for agent runtime, evaluation, quality measurement, and model optimization.
• Create tooling to identify evaluation strengths, coverage gaps, and unsupported user questions.
• Design signal pipelines for user frustration, agent failures, and quality issues.
• Define an AI benchmark using real HubSpot workloads.
• Evaluate new models across agents to improve quality and reduce cost.
• Make fine-tuning and task-specific optimization more repeatable.

What you'll need

• Deep experience with production machine learning, LLM, or AI infrastructure.
• Experience building large-scale systems where reliability, latency, quality, and cost matter.
• Platform-oriented approach rather than one-off solutions.
• Ability to work across infrastructure, product, backend, and ML teams.
• Experience with evaluation frameworks, model serving, fine-tuning, signal extraction, experimentation, feedback loops, or model optimization.
• Ability to influence senior engineers and leaders through technical credibility and judgment.

Details

• Remote in the USA.
• Annual cash compensation: $285,800–$457,300 USD.
• Engineering employees must attend a regional HubSpot office for in-person onboarding.