Senior Machine Learning Engineer, AI Automation

Senior Machine Learning Engineer, AI Automation

Mountain View, CA, USA, Full-time

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At Unity, we are building a world-class ad-tech ecosystem that connects billions of users with games and experiences they love. The Advertiser Growth team is at the heart of this mission, owning the online ads delivery stack, including auction, pacing, and bidding systems.

As a Senior Machine Learning Engineer, you will be a key contributor to modernizing our core infrastructure and building the backend systems for next-generation AI agents that revolutionize campaign experience. This role offers the chance to solve complex system challenges at massive scale while directly influencing Unity’s revenue growth.

• Design and deploy intelligent agentic systems that integrate large language models (LLMs) with enterprise data, tools, and workflows using frameworks like Lang Chain, Llama Index, and Semantic Kernel.
• Develop Retrieval-Augmented Generation (RAG) applications using tools like Azure AI Search, vector databases, and secure enterprise connectors to deliver contextual insights.
• Build and iterate on conversational agents that solve real-world problems, meet stakeholder needs, and deliver measurable business value.
• Deliver high-impact features by collaborating across teams, leading through ambiguity, and aligning technical solutions with business goals.
• Drive quality and performance through automated testing, monitoring, and data-driven evaluation of success criteria, user adoption, and operational efficiency.
• Mentor teammates and contribute to a culture of innovation, technical excellence, and continuous learning.

• You have a Bachelor’s/Master’s level degree in computer science or relevant engineering-related field or equivalent experience.
• You have 8-10+ years of software engineering experience, including 1-2+ years working on AI-powered systems or products.
• You have a good foundational knowledge of or hands‑on experience with LLM orchestration frameworks (e.g., Lang Chain, Llama Index, Semantic Kernel) and prompt engineering using LLM APIs.
• Deep GenAI Knowledge:
  Expertise in LLM integration, prompt engineering, and the technical trade-offs of different model architectures for specific ad-tech use cases.
• RAG & Data Engineering:
  Hands‑on experience with vector stores (e.g., Pinecone, Milvus, Weaviate), embedding models, and data orchestration for context‑aware AI.
• MCP Protocol Mastery:
  Familiarity with the Model Context Protocol (MCP) to build interoperable “plug‑and‑play” connections between AI models and local or remote data sources.
• Scalable Backend Foundations:
  Strong proficiency in Java, Go, or Python, with a background in building large-scale distributed systems and real-time data pipelines (Kafka, Flink, or Spark).
• Experimental Mindset: A “zero‑to‑one” approach to engineering, comfortable with rapid prototyping and iterative testing in a fast‑paced environment.

• Ad‑Tech Domain Context:
  Understanding of auction mechanics, bidding algorithms, or advertiser delivery funnels.

• Relocation support is not…