Machine Learning Engineer, AI Early Stage Project

Machine Learning Engineer, AI Early Stage Project

Software Engineering Mountain View, CA (HQ)

X is Alphabet’s moonshot factory with a mission of inventing and launching “moonshot” technologies that could someday make the world a radically better place. We are a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people. Our goal: 10x impact on the world’s most intractable problems, not just 10% improvement.

We approach projects that have the aspiration and riskiness of research with the speed and ambition of a startup. As an innovation engine, X focuses on repeatedly turning breakthrough‑technology ideas into the foundations for large, sustainable businesses.

We are an early‑stage project at X working to revolutionize the industrial world by making material transformation intelligent. Our mission is to reduce the massive waste in material harvesting and processing. This is a growing sector faced with numerous challenges including resource exhaustion, rising energy costs, and a sizable carbon footprint. We are building a system that combines sensing, multimodal AI, agentic digital twins, and advanced physics‑based simulation to automate the continuous optimization of complex industrial processes.

We are looking for a Machine Learning Engineer to build out the cognitive engine of our multi‑modal sense making platform for the industrial world. In this role, you will solve a massive translation problem by converting the messy, unstructured reality of industrial systems (Piping and , technical manuals, sensor data, and visual feeds) into structured, queryable Process Knowledge Graphs (PKGs). You will not just be training models.

You will be architecting Agentic RAG workflows where VLMs (Vision‑Language Models) and LLMs reason together to generate digital twins. You will bridge the gap between perception (Computer Vision), real‑time sensing, and reasoning (Graph‑based logic) to create digital value from complex real‑world sources.

• Pioneer Dynamic Knowledge Graph Systems: Design and implement state‑of‑the‑art systems that extract structured semantic meaning from complex real‑world environments, reconciling disparate data modalities to build and refine models that simulate physical systems.
• Develop Agentic Reasoning Architectures: Engineer sophisticated Agentic RAG frameworks where Large Language Models reason over graph structures to perform multi‑step logical deduction, enabling the system to formulate and solve complex optimization problems.
• Solve High‑Noise Data Challenges: Construct resilient data pipelines that handle ambiguity and disparate formats at scale, ensuring reliability across documents, images, and telemetry.
• Accelerate Research‑to‑Production: Bridge the gap between experimental ML research, partner‑oriented sprints, and scalable production systems, driving the technical roadmap from prototype to deployed pilot.

• Bachelor's degree in Computer Science, AI, Engineering, or equivalent practical experience.
• 3+ years of experience in software engineering and applied machine learning (Python, PyTorch, or JAX).
• Experience working with Large Language Models (LLMs) and Vision‑Language Models (VLMs) in applied settings, including prompt engineering, fine‑tuning, or RAG.
• Strong understanding of graph data structures, Knowledge Graphs (e.g., Neo4j, Network
  
  X), or Graph Neural Networks (GNNs), including handling unstructured and/or messy real‑world data such as documents, images, videos, scanned diagrams, and sensor feeds.
• Experience implementing LLM‑driven code generation pipelines, specifically utilizing function calling or tool‑use patterns where agents generate and execute code (e.g., Python, SQL, or Cypher) to interact with external environments or data stores.

• Experience with Agentic workflows (e.g., Lang Chain, Auto Gen) where models perform multi‑step reasoning.
• Experience with MLOps best practices, including model deployment, monitoring, and designing pipelines that allow distinct components to…