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X, The Moonshot Factory · 3 hours ago

Machine Learning Engineer, AI Early Stage Project

Mountain View, CA

Full-time

Onsite

Mid Level

$141K/yr - $200K/yr

3+ years exp

X, The Moonshot Factory is dedicated to inventing transformative technologies, and they are seeking a Machine Learning Engineer to enhance their multi-modal sensemaking platform for the industrial sector. The role involves architecting advanced systems to convert unstructured industrial data into structured knowledge graphs, enabling intelligent automation and optimization of complex processes.

AerospaceAugmented RealityAutomotiveRobotics

Responsibilities

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

Qualification

Machine LearningPythonGraph data structuresLarge Language ModelsVision-Language ModelsMLOpsComputer VisionReinforcement LearningAgentic workflowsData pipelinesSimulation logicTechnical documentationSoft skills

Required

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

Preferred

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