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Clera

Founding Engineer – ML Demand Generation

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

About this role

A Series A AI/ML platform company seeks a Founding Engineer to build machine learning systems that drive demand generation, lead scoring, and conversion optimization. You'll own the full ML lifecycle—from pipeline design to model deployment—while partnering with marketing and product teams to deliver measurable growth outcomes.

What you'll do

  • Build and deploy ML models for lead scoring, conversion prediction, and campaign performance optimization
  • Design and maintain production data pipelines for behavioral analytics, audience segmentation, and experimentation
  • Automate demand generation workflows including audience segmentation and personalized outreach
  • Collaborate with marketing and product teams to translate growth goals into ML solutions
  • Experiment with LLMs and generative AI for content personalization and outreach strategies
  • Establish data-driven frameworks for channel optimization and ROI tracking

What they're looking for

  • Python and ML frameworks (PyTorch or TensorFlow)
  • End-to-end ML pipeline design and deployment
  • Lead scoring and conversion prediction modeling
  • Data engineering and behavioral analytics
  • Experimentation and A/B testing frameworks
  • LLMs and generative AI applications
  • Marketing tech stack integration (HubSpot, Salesforce, ad APIs)
  • Growth analytics and user modeling

Benefits

  • Salary: $220,000–$300,000 USD annually
  • Early-stage equity opportunity
  • On-site role in Mountain View, CA with a Series A-funded company
  • High-impact founding engineer position with direct influence on company direction
  • Collaboration with cross-functional teams (marketing, product, leadership)
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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've built end-to-end—what features did you engineer and how did you measure impact?
  • Describe your experience designing production ML pipelines. What challenges did you encounter and how did you ensure reliability and scalability?
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