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Founding Engineer - ML Demand Generation

Clera · Remote, United States

Other EngineeringRemoteExternal listingfull-time12 minutes ago

About The Role

ABOUT THE ROLE

This is a founding-level ML engineering role at an AI/ML data and services company, sitting at the intersection of machine learning and growth. You'll build intelligent systems that directly drive user acquisition, engagement, and revenue — turning data science into measurable demand outcomes.

WHAT YOU'LL DO

  • Build ML models to optimize lead scoring, conversion prediction, and campaign performance.
  • Automate demand generation workflows, from audience segmentation to personalized outreach.
  • Design and maintain data pipelines for behavioral analytics, targeting, and experimentation.
  • Partner with marketing and product teams to translate growth goals into ML-driven solutions.
  • Experiment with LLMs, recommendation systems, and generative AI for content and outreach.
  • Establish data-driven frameworks for channel optimization and ROI tracking.

WHAT WE'RE LOOKING FOR

  • 3–10 years of hands-on ML engineering, data science, or growth analytics experience.
  • Strong Python skills with practical experience in PyTorch and/or TensorFlow for building and deploying models.
  • Proven track record with data-driven growth systems: lead scoring, conversion prediction, user modeling, or campaign optimization.
  • Experience integrating with marketing and CRM platforms (e.g. HubSpot, Salesforce) in ML-driven workflows.
  • Familiarity with advertising APIs (e.g. Google Ads, Meta Ads) for model-driven campaign optimization.
  • Experience with LLMs, recommender systems, and generative AI techniques.
  • Strong cross-functional communication skills to bridge technical and growth teams.
  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field — or equivalent practical experience.

COMPENSATION & BENEFITS

Base salary: $220,000 – $300,000 USD annually. Visa sponsorship is available for this role.

LOCATION

On-site, full-time in Mountain View, California, USA. Remote work is not available for this position.

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