Machine Learning Engineer, AI Early Stage Project
Listed on 2026-01-12
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IT/Tech
AI Engineer, Machine Learning/ ML Engineer, Data Scientist
Machine Learning Engineer, AI Early Stage Project
Join to apply for the Machine Learning Engineer, AI Early Stage Project role at X, The Moonshot Factory
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. 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 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 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:
Design state‑of‑the‑art systems that extract structured semantic meaning from complex real‑world environments and reconcile disparate data modalities. - Develop Agentic Reasoning Architectures:
Engineer sophisticated agentic RAG frameworks where LLMs reason over graph structures to perform multi‑step logical deduction, enabling complex optimization problems to be solved. - Solve High‑Noise Data Challenges:
Create gold standard digital models from noisy real‑world data, designing resilient data pipelines that handle ambiguity and disparate formats at scale. - Accelerate Research‑to‑Production:
Bridge the gap between experimental ML research and scalable production systems, driving the technical roadmap from 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, fine‑tuning, or RAG.
- Strong understanding of graph data structures, Knowledge Graphs, or Graph Neural Networks, including handling unstructured real‑world data such as documents, images, videos, scanned diagrams, and sensor feeds.
- Experience implementing LLM‑driven code generation pipelines with function calling or tool‑use patterns to interact with external environments or data stores.
It’d Be Great If You Had
- Experience with agentic workflows (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 interoperate seamlessly.
- Background in computer vision and VLMs, specifically object detection or segmentation on technical imagery or diagrams.
- Familiarity with reinforcement learning concepts applied to LLM post‑training or optimization problems.
- Demonstrated ability to build self‑correcting agentic loops where models iteratively write, execute, and debug code, particularly for generating…
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