Member of Technical Staff

Complement

$120K — $180K *
Manufacturing & Automotive
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of experience in AI system integration and data management.
  • Proven ability to work with legacy data sources including ERPs and custom tools.
  • Strong analytical skills for real-time simulation and optimization of manufacturing processes.
  • Experience in designing user interfaces for data visualization and decision-making.
  • Familiarity with mapping workflows and operational bottlenecks in factory settings.

Responsibilities

  • Build AI systems that integrate and unify diverse legacy data sources.
  • Run simulations to optimize manufacturing workflows and mitigate delays.
  • Identify and resolve operational bottlenecks within the production process.
  • Collaborate with factory operators to map and improve existing workflows.
  • Design user interfaces that provide generative, actionable insights for operators.

Benefits

  • Collaborative work environment with cross-functional teams.
  • Opportunity to impact real-world manufacturing operations.
  • Engagement with cutting-edge AI and data integration technologies.
  • Support for continuous learning and professional growth.
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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