AI Engineer for Production Systems & Robotics

AutoStore System, Inc; Atlanta, Northern (GA, KY); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Comprehensive medical, dental, vision

401(k) with employer match

Paid time off + holidays

Professional development

Life & disability insurance

Job summary

AutoStore is hiring an AI Engineer to help our Engineering team integrate AI capabilities into products. You will work cross-functionally as a subject-matter expert to accelerate AI initiatives and support scaled ML deployments across cloud environments.

The role emphasizes practical production ML, time-series forecasting, and optimization in hard-tech contexts. Collaboration across time zones and a strong emphasis on reliability are key.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field is required.
• Master's degree is preferred but not mandatory.
• Hands-on experience with LLM agent development and multi-step orchestration is essential.
• Experience deploying ML models on cloud infrastructure with cost/latency considerations.
• Strong background in optimization for routing or scheduling problems is desired.

Responsibilities

• Build and ship AI agent systems connected to data sources and tools.
• Deploy and operate AI on cloud infrastructure, managing cost and scalability.
• Apply ML optimization to routing and coordination of robotic fleets.
• Develop time-series models for operational telemetry and anomaly detection.
• Ensure model outputs reliably influence hardware behavior in physical systems.

Skills

ML engineering

LLM agent development

Cloud deployment

Data-driven optimization

Time-series forecasting

Distributed teams

Education

Bachelor's degree in CS/Engineering

Master's preferred

Tools

Python

TensorFlow

PyTorch

Cloud platforms

Job description

AutoStore is hiring an AI Engineer to help our Engineering team integrate AI capabilities into products. You will work cross-functionally as a subject-matter expert to accelerate AI initiatives and support scaled ML deployments across cloud environments.

The role emphasizes practical production ML, time-series forecasting, and optimization in hard-tech contexts. Collaboration across time zones and a strong emphasis on reliability are key.