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Deccan AI

Founding Engineer - ML Demand Generation

Posted Yesterday
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In-Office
Mountain View, CA
Mid level
In-Office
Mountain View, CA
Mid level
As a Founding ML Engineer, you'll develop ML models for lead optimization, automate demand generation processes, build data pipelines, and collaborate with teams to enhance growth through AI solutions.
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mouAbout Us

Deccan AI is a model training and eval startup headquartered in the Bay Area. We are founded by IIT Bombay, IIM Ahmedabad and ex-Google alumni, and work with some of the top AI frontier labs in the world, e.g. Google Deepmind, Snowflake, and many more. We are backed by Prosus Ventures, and our India delivery office is in Hyderabad. Most of our roles are on-site i.e. Bay Area / Hyderabad.
About the Role

We’re looking for a Founding ML Engineer focused on Demand Generation — someone who can blend data science, machine learning, and growth strategy to drive user acquisition and engagement through intelligent systems.

You’ll build AI-driven pipelines that power lead generation, personalization, and performance marketing — helping scale how we reach, convert, and retain our user base. Think of it as engineering meets growth, where models create measurable impact on demand.

What You’ll Do
  • Build ML models that optimize lead scoring, conversion prediction, and campaign performance.

  • Automate demand generation workflows — from audience segmentation to personalized outreach.

  • Design data pipelines for behavioral analytics, targeting, and experimentation.

  • Collaborate with marketing and product teams to translate growth goals into measurable ML solutions.

  • Experiment with LLMs, recommendation systems, and generative AI for content and outreach.

  • Establish data-driven frameworks for channel optimization and ROI tracking.

What You’ll Bring
  • 3–7 years of experience in ML engineering, data science, or growth analytics.

  • Strong command of Python, PyTorch / TensorFlow, and ML pipelines.

  • Proven experience with data-driven growth systems — user modeling, scoring, recommendation, or automation.

  • Understanding of marketing tech stacks (HubSpot, Salesforce, Meta/Google Ads APIs, etc.) is a plus.

  • A builder’s mindset — you move fast, experiment often, and own measurable impact.

  • Curiosity for how AI can drive business outcomes beyond traditional ML boundaries.

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