AI Infrastructure Engineer

Dormont Manufacturing Co; Menlo Park (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Dormont Manufacturing Co is looking for an AI Infrastructure Engineer in Menlo Park, California to design and operate data systems for MatterOS. You'll manage edge-to-cloud data pipelines, GPU infrastructure for model training, and ensure contextual data capture for AI applications.

The ideal candidate has 3+ years of experience in ML infrastructure or data engineering, proficiency in Python, and familiarity with distributed data systems. This role offers the chance to build autonomous manufacturing infrastructure from scratch.

Qualifications

• 3+ years of experience in ML infrastructure, MLOps, or data engineering.
• Strong command of distributed data systems.
• Experience with GPU cluster management and distributed training.

Responsibilities

• Design and maintain the edge-to-cloud data pipeline.
• Build and manage GPU compute infrastructure.
• Implement the data collection layer for modular assembly workcells.
• Develop feature engineering pipelines for AI models.
• Manage model deployment to edge hardware.
• Build observability systems for model performance.
• Collaborate with AI researchers on infrastructure specifications.

Skills

ML infrastructure experience

Distributed data systems

GPU cluster management

Proficiency in Python

Systems thinking

Tools

Kafka

Flink

InfluxDB

SLURM

Job description

ABOUT MATTER

Matter is building the AI-native autonomy stack for physical manufacturing in the United States. We operate our own factories, deploy our own software, and collect data from every stage of production — from CAD intake to finished goods.

Our platform, MatterOS, is the unified software layer for factory operations, process orchestration, and autonomy deployment. The data pipeline that feeds it — from machine telemetry on the floor to model training in the cloud — is the infrastructure you will build and own.

THE ROLE

We are hiring an AI Infrastructure Engineer to design and operate the data and compute systems that power MatterOS and our Sim2Real training pipeline. You will work across edge computing, cloud training infrastructure, and the data pipelines that make our “Smart Data” strategy real.

Your job is to ensure that every data point — from a torque sensor reading to a camera frame — is tagged with the machine ID, process state, and production context that makes it trainable.

WHAT YOU’LL DO

• Design and maintain the edge-to-cloud data pipeline with semantic context preserved end-to-end
• Build and manage GPU compute infrastructure for VLA model training, experiment tracking, and distributed training workflows
• Implement the data collection layer for 100% capture from modular assembly workcells, including camera feeds, sensor streams, machine state, and process metadata
• Develop feature engineering pipelines that transform raw operational data into structured training inputs for AI models
• Manage model deployment to edge hardware in the factory: latency, versioning, rollback, and monitoring in production
• Build observability systems that surface model performance degradation, data drift, and equipment anomalies in real time
• Collaborate with AI researchers to translate model requirements into infrastructure specifications and vice versa

WHAT WE’RE LOOKING FOR

• 3+ years of experience in ML infrastructure, MLOps, or data engineering in a production environment
• Strong command of distributed data systems: Kafka, Flink, or equivalent; time-series databases (InfluxDB, TimescaleDB, or similar)
• Experience with GPU cluster management and distributed training (SLURM, Ray, or Kubernetes-based)
• Familiarity with industrial protocols: OPC UA, MQTT, Modbus (or willingness to learn quickly)
• Proficiency in Python; comfort with C++ or Rust for performance‑critical edge components is a plus
• Systems thinking: you understand that data quality, not data volume, is what makes AI work in constrained physical environments

NICE TO HAVE

• Experience with NVIDIA Isaac Sim, ROS2, or edge AI deployment (Jetson, FPGA, or similar)
• Background in industrial IoT or factory automation systems
• Familiarity with model serving frameworks (Triton, TorchServe, or ONNX Runtime)

WHY MATTER

Most AI infrastructure roles are about keeping existing systems running. At Matter, you are building the infrastructure from scratch for a category that doesn’t fully exist yet: autonomous physical manufacturing.