Machine Learning EngineerAionia Group • Mountain View, California, United States
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Machine Learning Engineer
Aionia Group
- Mountain View, California, United States
- Mountain View, California, United States
Über
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.
What You’ll Build
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.
Qualifications
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).
Required
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. LangGraph), or RAG systems.
Experience with ML frameworks (PyTorch, TensorFlow) or MLOps infrastructure (MLflow, Kubernetes, serving systems).
Product‑mindful with a strong focus on end‑user experience.
Nice to Have
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.
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Sprachkenntnisse
- English
Hinweis für Nutzer
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