Machine Learning Engineer

Machine Learning Engineer

One of the first ML Engineers at a 25-person rocketship automating a $1T industry. Design and build the entire ecosystem where autonomous AI agents live, learn, and operate — with greenfield scope across the full stack.

Our client was founded by technologists and operators who spent over a decade building ad platforms and e-commerce engines. They are pioneering autonomous growth agents that bring advanced data science and machine learning to every business — targeting a $1T global performance marketing industry.

A rapidly growing network of D2C brands rely on their intelligent agents to simplify marketing complexity, uncover actionable insights, and autonomously drive measurable results. Customers are already seeing a 40% performance lift.

World‑class advisor — former President/GM at a top global technology company, with direct experience building one of the largest digital advertising platforms in the world.

• Design and build the core agentic platform — the engine that allows the company to craft, manage, and continuously improve autonomous agents.
• Architect the foundational data and signal platform using a modern lakehouse architecture with robust pipelines and ML serving systems.
• Build a suite of powerful, reliable, and safe tool integrations that allow agents to interact with the world.
• Develop customer‑facing applications including a chat UI where users collaborate with AI agents.
• Build MLOps infrastructure for training, fine‑tuning, and deploying state‑of‑the‑art reasoning models in collaboration with data scientists.

• Background at well‑known technology companies — Big Tech or highly reputable startups — ideally in ads, search, or recommendation systems. Best fit is Big Tech + startup combination (top Big Tech companies in ads/search ideal).
• Hands‑on ML modeling and training experience — not just infrastructure.
• Master’s or PhD in CS, or Bachelor’s + 2+ years professional software engineering.
• Ability to work from the Mountain View, CA office (hybrid available for SF‑based candidates).

• 3–8 years of ML engineering experience with production‑level code (ML platform or modeling background both acceptable).
• Hands‑on experience with LLMs, agentic frameworks (e.g. Lang Graph), or RAG systems.
• Experience with ML frameworks (PyTorch, Tensor Flow) or MLOps infrastructure (MLflow, Kubernetes, serving systems).
• Product‑mindful with a strong focus on end‑user experience.

• Prior experience at a high‑growth, venture‑backed startup.
• Degree from a Top 30 university or equivalent tier‑1 company experience.
• Data engineering experience — ETL/streaming pipelines, Spark, Airflow, dbt, or lakehouse architecture.