AI Production Scheduling Optimizer

Synta Cloud

Remote · $80k – $100k / year · Full-time · Posted 4 months ago

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About the role

Synta Cloud works with industrial manufacturers who run complex multi-machine production environments. Scheduling production runs across dozens of machines, shift patterns, and SKUs is a combinatorial optimization problem — and right now, most of our clients are solving it with spreadsheets. That's the gap we're closing. In this role, you'll work alongside our senior operations research engineer to implement ML-assisted production scheduling models. You'll analyze historical scheduling data, build models that predict machine bottlenecks and changeover times, and help translate outputs into actionable schedules that floor managers can use. This is a junior position and a great entry point if you studied operations research, industrial engineering, or applied math. We'll teach you what you don't know about manufacturing domain knowledge. In return, we expect intellectual curiosity, strong analytical fundamentals, and the confidence to ask questions when you're stuck.

Fully remote.

Responsibilities

Analyze historical production data to identify scheduling patterns and bottlenecks
Build ML models for machine bottleneck and changeover time prediction
Collaborate with senior OR engineer to implement scheduling optimization algorithms
Generate production schedule outputs in formats usable by floor management teams
Document modeling assumptions and schedule constraints

Requirements

1+ year experience in data analysis, operations research, or industrial engineering
Solid Python and SQL skills
Basic understanding of ML and optimization concepts
Strong analytical and quantitative background
Interest in manufacturing or industrial operations is a plus