Staff ML Engineer: Production AI for Edge Manufacturing

Firestorm; San Diego (CA); Sur place; USD 150 000 - 220 000; Plein temps

Avantages offerts par ce poste

Medical benefits

401(k) plan

Equity grants

Unlimited PTO

Holiday calendar

Résumé du poste

Firestorm is hiring a Staff Machine Learning Engineer to build the applied AI systems behind Crucible. You will productionize optimization and ML models, integrate foundation models into the platform, and create AI-assisted workflows grounded in Crucible's data and system model.

This hands-on role focuses on shipping ML capabilities into real products, ensuring reliability, measurable impact, and practical usability post-prototype.

Qualifications

• 5+ years building and shipping production software or ML systems.
• Hands-on experience with AI/ML systems and production reliability.
• Strong Python and software engineering fundamentals.
• Experience with LLMs or foundation models in production.

Responsabilités

• Build AI capabilities in Crucible to surface insights and guide decisions.
• Productionize optimization, forecasting, and other models.
• Design model interfaces, retrieval, tooling, orchestration, and production services.
• Evaluate models based on capability, reliability, latency, and cost.
• Support AI in cloud, air-gapped, and edge deployments.
• Collaborate with manufacturing, supply chain, and product teams.
• Set technical direction for applied AI within Crucible.

Connaissances

Production software/ML systems

Python

LLMs / Foundation models

Model evaluation & monitoring

Software engineering fundamentals

Experimentation & debugging

Performance optimization

Outils

PyTorch/TensorFlow

Open-source models

GPU deployment

Description du poste

Firestorm is hiring a Staff Machine Learning Engineer to build the applied AI systems behind Crucible. You will productionize optimization and ML models, integrate foundation models into the platform, and create AI-assisted workflows grounded in Crucible's data and system model.

This hands-on role focuses on shipping ML capabilities into real products, ensuring reliability, measurable impact, and practical usability post-prototype.