Remote AI Systems Engineer - Multi-Hop Math Reasoning

OpenTrain AI, Inc.; United States; Remote; USD 83,000 - 131,000; Full time

Benefits offered by this job

Remote work worldwide

Four-week contractor engagement

Overlap with Pacific Time

Job summary

OpenTrain AI, Inc. seeks a contractor to turn mathematical proofs and dense academic material into structured software systems, automated pipelines, and reusable tools for AI agents.

You will build multi-step agent workflows for reasoning and validation, translating proofs into technical specs and pipelines. You will work with researchers to create modular, reliable software and evaluate execution traces to improve systems and outputs.

Qualifications

• Advanced Python proficiency and practical experience with LLM orchestration.
• Experience designing multi-step agent workflows for complex reasoning.
• Ability to translate math/CS concepts into structured software systems.

Responsibilities

• Design, build, and optimize multi-step agent workflows for multi-hop reasoning, hypothesis generation, and technical validation.
• Translate mathematical proofs and academic material into technical specs and automated pipelines.
• Develop architecture frameworks, tools, and memory systems for STEM-focused AI agents.
• Evaluate agent performance for accuracy, reliability, and traceability across complex execution steps.
• Collaborate with mathematical researchers and subject-matter experts on reusable AI workflows.

Skills

Python proficiency

Large language model orchestration

Multi-hop reasoning

Agent workflows design

Software engineering

Tools

LangGraph

AutoGen

CrewAI

Job description

OpenTrain AI, Inc. seeks a contractor to turn mathematical proofs and dense academic material into structured software systems, automated pipelines, and reusable tools for AI agents.

You will build multi-step agent workflows for reasoning and validation, translating proofs into technical specs and pipelines. You will work with researchers to create modular, reliable software and evaluate execution traces to improve systems and outputs.