Join a seed-stage AI startup backed by Kleiner Perkins to build the core agentic platform for autonomous growth agents. You will architect the platform, data systems, and tool integrations, working across the full stack. Requires solid ML engineering experience, startup/big tech background, and production-level Python.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
Machine Learning Engineer | Seed-Stage AI Startup | $25M Raised | Mountain View (Hybrid)
A seed-stage AI company backed by Kleiner Perkins is looking for a Machine Learning Engineer to join a team of 27 and help build the future of autonomous growth agents.
The company This startup is tackling the $1T performance marketing industry with AI agents that autonomously drive measurable results for brands. They've raised a $25M seed round from a top-tier VC, they're already delivering 40%+ lift for customers, and the waitlist is growing. The founder is the former VPE of Instacart. Their advisor built one of the biggest ad platforms in history.
The role
- You'll architect the core agentic platform from the ground up. This isn't a narrow modelling job — you'll work across the entire stack:
- Design and build the agentic platform that powers autonomous agents
- Architect the data and signal platform using modern lakehouse architecture
- Build reliable tool systems that let agents interact with the real world
- Develop customer-facing applications including chat UI
- Collaborate with data scientists on MLOps infrastructure for training and deploying reasoning models
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What they're looking for
- Solid experience of ML engineering experience
- Experience of working at a startup and big tech
- Production-level Python
- Experience across some of: PyTorch, TensorFlow, LangGraph, Kubernetes, MLflow, Spark, Airflow, dbt
- Product-minded, systems-level thinker comfortable with ambiguity
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What's on offer
- $160-200K base + competitive equity
- Visa sponsorship available
- Hybrid in Mountain View, flexible schedule for SF-based candidates
- Early-stage impact — you'd be roughly employee #30
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This is a rare chance to shape the technical foundation of a well-funded startup before the rocket ship takes off.
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