ML Engineer Forward Deployed (USA)

Job Description

Job Description

Salary : Competitive

Description

Were building Orbital, an industrial AI system that runs live in refineries and upstream assets, ingesting sensor data, running deep learning + physics hybrid models, and serving insights in real time. As a Forward Deployed ML Engineer, youll sit at the intersection of research and deployment : turning notebooks into containerised microservices, wiring up ML inference pipelines, and making sure they run reliably in demanding industrial environments.

This role is not just about training models. Youll write PyTorch code when needed, package models into Docker containers, design message-brokered microservice architectures, and deploy them in hybrid on-prem / cloud setups. Youll also be customer-facing : working with process engineers and operators to integrate Orbital into their workflows.

Location :

You will be based in the U.S. or eligible to work there and ideally in Houston or willing to travel extensively to there.

Core Responsibilities

Model Integration & Engineering

• Take research models (time-series, deep learning, physics-informed) and productionise them in PyTorch.
• Wrap models into containerised services (Docker / Kubernetes) with clear APIs.
• Optimise inference pipelines for latency, throughput, and reliability.

Microservices & Messaging

Forward Deployment & Customer Integration

Software Engineering Best Practices

Requirements

What Success Looks Like