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Clera

Founding Engineer – ML Demand Generation

remote (Remote)$220k–$300kfulltimemidAdded today

About this role

Join a Series A AI startup as a Founding ML Engineer to build intelligent systems that drive customer acquisition and growth. You'll develop ML models for lead scoring, conversion prediction, and campaign optimization while collaborating with marketing and product teams to scale demand generation.

What you'll do

  • Build and deploy ML models for lead scoring, conversion prediction, and campaign performance optimization
  • Design end-to-end data pipelines for audience segmentation, behavioral analytics, and experimentation
  • Integrate with marketing platforms (HubSpot, Salesforce) and advertising APIs (Google Ads, Meta Ads) for ML-driven workflows
  • Experiment with LLMs, recommendation systems, and generative AI for content creation and personalized outreach
  • Establish data-driven frameworks for channel optimization and ROI tracking
  • Collaborate with marketing and product teams to translate growth goals into measurable ML solutions

What they're looking for

  • Python programming
  • PyTorch and/or TensorFlow
  • Machine learning (lead scoring, conversion prediction, recommendation systems)
  • Data pipeline design and implementation
  • Large language models and generative AI
  • HubSpot and Salesforce integrations
  • Google Ads API and Meta Ads API
  • A/B testing and experimentation frameworks

Benefits

  • Salary: $220,000–$300,000 per year
  • Founding-team equity opportunity
  • Visa sponsorship available
  • Work at a rapidly scaling Series A AI startup
  • Backed by prominent institutional investors
  • Access to leading AI frontier labs and enterprise partnerships
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Walk us through a lead scoring or conversion prediction model you built—what features were most predictive and how did you validate performance?
  • Describe your experience integrating ML models with CRM and advertising platforms in production. What challenges did you encounter?
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