Senior ML Engineer: Remote LLM Reasoning & Planning Expert

Alignerr

Charlotte (AR)

On-site

USD 110,000 - 207,000

Part time

4 hours ago
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Benefits offered by this job

Fully remote
Flexible contract
Async work

Job summary

Alignerr is seeking a Senior Machine Learning Expert to lead the creation of high-fidelity reasoning traces that guide LLMs to think step by step, plan tool use, and make decisions in complex tasks. This is a fully remote, flexible contract role for senior ML practitioners who understand model behavior at a deep level.

You will author traces, document structured decision flows, review and improve existing data, and develop data strategies that improve model reliability.

Qualifications

  • Deep familiarity with large language models and how they reason, plan, and decide.
  • Ability to decompose complex tasks into structured, logical steps.
  • Experience with evaluating model behavior and data shaping effects.
  • Comfort working asynchronously in a remote setup.

Responsibilities

  • Author high-fidelity reasoning traces that show LLMs how to plan, use tools, and decide on complex tasks.
  • Design and document structured decision-making flows reflecting real-world problem solving at senior level.
  • Review and improve traces for coherence, planning quality, and tool-use accuracy.
  • Develop data strategies to improve multi-step scenario reliability for AI models.
  • Apply architectural insights to ensure data drives meaningful model improvements.

Skills

LLMs
Model reasoning
ML training methodologies
Independent remote work

Job description

Alignerr is seeking a Senior Machine Learning Expert to lead the creation of high-fidelity reasoning traces that guide LLMs to think step by step, plan tool use, and make decisions in complex tasks. This is a fully remote, flexible contract role for senior ML practitioners who understand model behavior at a deep level.

You will author traces, document structured decision flows, review and improve existing data, and develop data strategies that improve model reliability.

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