Sr. Machine Learning Engineer I

HubSpot

Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$155,900–$233,900 / yr
Posted
14 days ago

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $221k
This role $195k
$143k most similar roles pay here $275k

This role pays less than 74% of similar roles. Most pay $188,625–$253,000 — the shaded band above. At the midpoint, this role pays about $195k versus about $221k for comparable roles.

Based on 240 similar postings.

Employer

About HubSpot

HubSpot is an AI-powered customer relationship management (CRM) platform that unifies marketing, sales, customer service, and operations.

HubSpot currently has 5 open roles on FindRole.

Listed pay typically runs $155,900–$233,900 across 5 roles with salary data.

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View all roles at HubSpot

At a glance

TL;DR · Sr. Machine Learning Engineer I

As a Senior Machine Learning Engineer on HubSpot's Agent Platform team, you will be part of the Breeze Studio project, focusing on building and improving AI agents that operate within HubSpot’s CRM system. Your daily tasks include designing experiments to enhance agent performance, developing evaluation infrastructure, optimizing large language model (LLM) inference for efficiency and cost-effectiveness, and collaborating with product teams to ensure high-quality models are deployed. You will work closely with LLM vendors like OpenAI, translating customer quality issues into actionable ML solutions. The role requires strong Python skills, experience with frameworks such as PyTorch, VLLM, and DSPy, and the ability to operate in an early-stage environment with significant autonomy and impact. This strategic position is crucial for HubSpot’s AI-first CRM direction, offering unique challenges at the forefront of LLM capabilities.

What you'll do

  • Design and run experiments to enhance agent quality through better tool use, reasoning, and outputs.
  • Build and maintain evaluation infrastructure to measure model performance and catch regressions proactively.
  • Optimize LLM inference for latency, cost efficiency, and model routing while ensuring high quality.
  • Partner with product teams on selecting models and conducting performance benchmarking.
  • Own the end-to-end lifecycle of ML models from research to production deployment.

What we're looking for

  • 5+ years of experience in a dedicated ML Engineer role.
  • Strong Python skills with experience using PyTorch and LLM optimization frameworks.
  • Hands-on experience working with large language models in production environments.
  • Ability to design, run experiments, and ship improvements for agent quality issues.
  • Comfortable working independently in an early-stage product environment.

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