Clera
Founding ML Engineer – Demand Generation
About this role
A Series A AI/ML infrastructure company seeks a Founding ML Engineer to build machine learning systems that power demand generation, lead scoring, and campaign optimization. You'll bridge ML and growth by developing intelligent automation for user acquisition and conversion, working cross-functionally with marketing and product teams.
What you'll do
- Build and deploy ML models for lead scoring, conversion prediction, and campaign performance optimization
- Design and maintain data pipelines for behavioral analytics, targeting, and A/B experimentation
- Automate demand generation workflows including audience segmentation and personalized outreach at scale
- Experiment with LLMs, recommendation systems, and generative AI for content creation and outreach
- Collaborate with marketing and product teams to translate growth objectives into ML solutions
- Establish data-driven frameworks for channel optimization and ROI tracking
What they're looking for
- Python
- PyTorch or TensorFlow
- ML model development and deployment
- Data pipeline design and engineering
- HubSpot, Salesforce, Google Ads API, Meta Ads API
- LLMs and generative AI
- A/B testing and experimentation frameworks
- Behavioral analytics and audience segmentation
Benefits
- Competitive salary: $220,000–$300,000 USD annually
- Early-stage equity as founding team member
- Opportunity to shape ML strategy at Series A stage
- Cross-functional collaboration with marketing and product
- Work on cutting-edge AI/ML infrastructure
Opens the official application on the employer’s site. No login required.
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.
View all jobs at CleraLikely interview questions
- Describe a lead scoring or conversion prediction model you built in production. How did you measure its impact on revenue or conversion rate?
- Tell us about a time you integrated marketing APIs (HubSpot, Salesforce, Google Ads, or Meta Ads) into a data pipeline. What challenges did you face?