Machine Learning Engineer

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: Machine Learning Engineer

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

• Build End-to-End Agent Workflows: You will design and implement multi-step agentic systems where LLMs and VLMs reason over unstructured inputs, call tools, generate and execute code, and iteratively self-correct against automated verification. You will own the full loop — from perception to structured output to validated action.
• Create Verifiable Evaluation Infrastructure: You will build evaluation systems where every agent output is automatically scored against ground truth — not with LLM-as-judge, but with deterministic, domain-grounded checks. These evaluation signals will drive prompt iteration, model selection, and feed directly into RL-based post-training pipelines for foundation model improvement.
• Fuse Multi-Modal Perception into Structured Knowledge: You will engineer pipelines that reconcile noisy, conflicting signals from multiple models and data sources (vision, language, documents, sensors) into unified structured representations. You will replace hand-tuned heuristics with learned calibration, ensuring confidence scores are meaningful and actionable.
• Ship Agents to Production: You will bridge the gap between research prototypes and deployed systems operating on real partner data. You will build regression suites, implement feedback loops where human corrections become training data, and ensure systems degrade gracefully under noisy, incomplete, or adversarial inputs.

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) or Vision-Language Models (VLMs) in applied settings, including prompt engineering, fine-tuning, or RAG.
• Strong understanding of Graph data structures, Knowledge Graphs, or graph-based reasoning, including handling unstructured real-world data such as documents, images, scanned diagrams, and sensor feeds.
• Experience with agentic workflows (e.g., LangChain, LangGraph, AutoGen, CrewAI) where models perform multi-step reasoning with tool use or code execution.
• A "0 to 1" mindset with the ability to thrive in ambiguity and define technical roadmaps.

It’d Be Great If You Had These

• Hands-on experience building LLM-driven code generation pipelines with 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 implementing self-correcting workflows where model outputs are validated programmatically and failures are fed back as context for retry.
• Strong understanding of evaluation design for generative AI systems, with emphasis on automatic, reproducible, and domain-grounded metrics.
• Familiarity with RL applied to LLM post-training (RLHF, RLVR), particularly using automated verifiers as reward signals.
• Experience with VLMs for document or diagram understanding, or multi-model fusion combining vision and language outputs.
• Interest in industrial automation, physics-based simulation, or AI for Science applications.

Key skills/competency

• Machine Learning
• Artificial Intelligence
• Large Language Models (LLMs)
• Vision-Language Models (VLMs)
• Agentic Workflows
• Knowledge Graphs
• Python
• PyTorch
• JAX
• Computer Vision