Founding Engineer - ML Demand Generation

About the Role This is a founding-level ML engineering role at the intersection of data science, growth strategy, and AI — sitting at the core of a fast-growing AI/ML data and services company that works with leading frontier AI labs. You'll build intelligent systems that drive user acquisition, lead conversion, and campaign performance, directly shaping how the company scales its reach and customer base. What You'll Do Build ML models to optimize lead scoring, conversion prediction, and campaign performance. Automate demand generation workflows covering audience segmentation and personalized outreach. Design and maintain data pipelines for behavioral analytics, targeting, and A/B experimentation. Partner with marketing and product teams to translate growth goals into measurable ML solutions. Experiment with LLMs, recommendation systems, and generative AI for content creation 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 experience in demand generation, growth marketing, or data-driven marketing domains. Strong proficiency in Python, with practical experience using PyTorch and/or TensorFlow to build and deploy models. Experience integrating marketing and CRM platforms (e.g. HubSpot, Salesforce) into ML-driven workflows. Familiarity with advertising APIs (e.g. Google Ads API, Meta Ads API) for model-driven campaign optimization. Proven ability to develop ML solutions for lead scoring, conversion prediction, and performance optimization. Hands-on experience with LLMs, recommender systems, and generative AI techniques. Strong cross-functional communication skills and a track record of delivering measurable growth impact. Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field — or equivalent practical experience. Compensation & Benefits Base salary range: $220,000 – $300,000 USD annually. Visa sponsorship is available for this role. Location This is a fully on-site role based in Mountain View, California, US .