Machine Learning Engineer, AI Early Stage Project

Software EngineeringMountain View, CA (HQ)

About X:

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.

About the Team

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.

About the Role

We are looking for a Machine Learning Engineer to build out the cognitive engine of our multi-modal sensemaking platform for the industrial world. In this role, you will solve a massive translation problem by converting the messy, unstructured reality of industrial systems (P&ID diagrams, 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.

How you will make 10x impact:

  • Pioneer Dynamic Knowledge Graph Systems: You will design and implement state-of-the-art systems that extract structured semantic meaning from complex real world environments. You will solve the critical challenge of reconciling disparate data modalities, e.g., physical asset detection, sensor data, quality reporting, leveraging these inputs to build and refine models which simulate physical systems.
  • Develop Agentic Reasoning Architectures: You will engineer sophisticated Agentic RAG frameworks where Large Language Models reason over graph structures to perform multi-step logical deduction. This will enable the system to formulate and solve complex optimization problems.
  • Solve High-Noise Data Challenges: You will tackle the engineering complexity of creating gold standard digital models from noisy real-world data. You will design resilient data pipelines that handle ambiguity and disparate formats at scale, ensuring reliability across documents, images, and telemetry.
  • Accelerate Research-to-Production: You will bridge the gap between experimental ML research, partner-oriented sprints to demonstrate value, and scalable production systems, driving the technical roadmap from initial prototype to deployed pilot.

What you should have:

  • 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 or fine-tuning or RAG.
  • Strong understanding of Graph data structures, Knowledge Graphs (e.g., Neo4j, NetworkX), or Graph Neural Networks (GNNs), including the handling of 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.

It’d be great if you had these:

  • Experience with Agentic workflows (e.g., LangChain, AutoGen) where models perform multi-step reasoning.
  • Experience with MLOps best practices, including model deployment, monitoring, and designing pipelines that allow distinct components to interoperate seamlessly.
  • Background in Computer Vision and VLMs, specifically object detection or segmentation on technical imagery or diagrams.
  • Familiarity with Reinforcement Learning (RL) concepts, particularly as applied to LLM post-training or optimization problems.
  • Demonstrated ability to build self-correcting agentic loops where models iteratively write, execute, and debug code (e.g., "Code Interpreter" patterns), particularly for generating simulation logic or automating data analysis in scientific/engineering contexts.
  • Interest in industrial automation, physics-based simulation, or AI for Science applications.
  • A "0 to 1" mindset with the ability to thrive in ambiguity and define technical roadmaps.

The US base salary range for this full-time position is $141,000 - $200,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.

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If you have a disability or special need that requires accommodation, please contact us at x-accommodation-request@x.team.

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