Member of Technical Staff

Complement

$100K — $150K *
Manufacturing & Automotive
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of experience in data integration and AI system development.
  • Proficiency working with a variety of data sources including ERPs and custom tools.
  • Strong understanding of manufacturing operations and workflows.
  • Experience in running simulations and optimization processes.
  • Familiarity with designing user interfaces for operational efficiency.

Responsibilities

  • Build and integrate AI systems that connect to legacy data sources.
  • Conduct real-time simulations to assess factory operational efficiency.
  • Identify and mitigate operational bottlenecks in production processes.
  • Collaborate with factory operators to map workflows and validate interventions.
  • Design generative interfaces to provide actionable production insights.

Benefits

  • Flexible working hours to accommodate dynamic project needs.
  • Opportunities for professional development in cutting-edge AI technologies.
  • Collaborative work environment with hands-on involvement in manufacturing operations.
  • Access to the latest tools and technologies for data integration and analysis.
Full Job Description


Core Roles
Data & Integration

Build AI systems that automatically connect to any legacy data source - ERPs, sensors, spreadsheets, custom tools - and turn them into a unified, real-time view of factory operations.

Simulation & Optimization

Run real-time simulations over the global factory state: balance work order demand/load, mitigate delay propagation, identify operational bottlenecks, and justify scheduling prioritizations by quantifying true downstream profit impact.

Manufacturing Systems

Embed with factory operators to map workflows, define and validate interventions, and feed real constraints back so recommendations work in practice.

Human Computer Interaction

Design just-in-time, generative interfaces to make production legible and deliver clear recommendations.

Who We're Looking For

People who can ship to messy, dynamic environments and are comfortable with the entire abstraction stack: from raw data and high-dimensional models to real-world constraints and quantifiable business impact. We care about what you've built under ambiguity and the impact you made.

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