Founding ML Engineer – Demand Generation

Jobtailor; Mountain View (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

Jobtailor in Mountain View, CA seeks an experienced ML engineer to advance growth analytics by building scalable models for lead scoring, conversion prediction, and campaign optimization. You will partner with marketing and product teams to translate growth objectives into ML solutions.

You bring 3–10 years of hands-on ML or data analytics experience, strong Python skills, and hands-on work with marketing APIs and ML frameworks. Authorization to work in the United States is required.

Qualifications

• 3–10 years of hands-on ML engineering, data science, or growth analytics experience.
• Direct exposure to demand generation, lead scoring, or campaign optimization in a production environment.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Proven experience building and deploying data pipelines for behavioral analytics, segmentation, and experimentation.
• Hands-on experience with marketing technology and APIs: HubSpot, Salesforce, Google Ads API, Meta Ads API.
• Experience experimenting with LLMs, recommendation systems, or generative AI for content and outreach.
• A builder's mindset — you move fast, experiment often, and own measurable outcomes.
• Must be authorized to work in the United States without employer sponsorship.

Responsibilities

• Build and deploy ML models for lead scoring, conversion prediction, and campaign performance optimization.
• Automate demand generation workflows — from audience segmentation to personalized outreach at scale.
• Design and maintain data pipelines for behavioral analytics, targeting, and A/B experimentation.
• Collaborate cross-functionally with marketing and product to translate growth objectives into ML solutions.
• Experiment with LLMs, recommendation systems, and generative AI for content creation and outreach automation.
• Establish data-driven frameworks for channel optimization and ROI tracking.

Skills

Python

ML engineering

Growth analytics

Tools

PyTorch

TensorFlow

Job description

• Build and deploy ML models for lead scoring, conversion prediction, and campaign performance optimization.
• Automate demand generation workflows — from audience segmentation to personalized outreach at scale.
• Design and maintain data pipelines for behavioral analytics, targeting, and A/B experimentation.
• Collaborate cross-functionally with marketing and product to translate growth objectives into ML solutions.
• Experiment with LLMs, recommendation systems, and generative AI for content creation and outreach automation.
• Establish data-driven frameworks for channel optimization and ROI tracking.

Requirements

• 3–10 years of hands‑on ML engineering, data science, or growth analytics experience.
• Direct exposure to demand generation, lead scoring, or campaign optimization in a production environment.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Proven experience building and deploying data pipelines for behavioral analytics, segmentation, and experimentation.
• Hands‑on experience with marketing technology and APIs: HubSpot, Salesforce, Google Ads API, Meta Ads API.
• Experience experimenting with LLMs, recommendation systems, or generative AI for content and outreach.
• A builder's mindset — you move fast, experiment often, and own measurable outcomes.
• Must be authorized to work in the United States without employer sponsorship.

Core Competencies

Demonstrates expertise in building and deploying ML models for lead scoring and campaign optimization, with strong proficiency in Python and ML frameworks. Proven ability to design data pipelines and automate demand generation workflows while collaborating cross‑functionally to achieve growth objectives.