Senior Machine Learning Engineer

Senior Machine Learning Engineer (AI Training)
About The Role
What if your deepest ML expertise — the kind built from years of debugging models, engineering features, and decomposing hard problems — could directly shape how the next generation of AI systems reason and make decisions?
We're looking for Senior Machine Learning Engineers to author high-fidelity reasoning traces for large language models. This means writing structured, step-by-step records of how an intelligent system should plan, use tools, and arrive at decisions when tackling complex, real-world technical tasks. The data you create trains LLMs to reason more reliably — and your senior-level insight is exactly what makes the difference between traces that are merely adequate and traces that are exceptional.
This is a fully remote, flexible contract role built for experienced ML practitioners who want to work at the frontier of AI development on their own terms.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Author complex, high-fidelity reasoning traces that capture how an LLM should plan, reason, and act when solving sophisticated technical tasks
Break down intricate problems into clear, logical, and well-documented decision sequences
Document tool use, planning strategies, and multi-step reasoning in structured formats
Review and provide expert feedback on traces created by other contributors
Design data strategies that help models navigate ambiguous, multi-step, real-world scenarios
Apply your understanding of LLM evaluation and training to ensure traces drive meaningful model improvement
Who You Are
Experienced ML practitioner with deep knowledge of model reasoning, training pipelines, or LLM behavior
Skilled at decomposing hard problems into structured, logical steps — and explaining your thinking clearly
Familiar with LLM evaluation methodologies and what makes a model's decision process trustworthy
Detail-oriented and rigorous — you set a high bar for quality and consistency
Comfortable working independently in an asynchronous, remote environment
Nice to Have
Prior experience with data annotation, data quality pipelines, or AI evaluation systems
Top-tier Kaggle competition results (Grandmaster or Master level) demonstrating advanced model performance and feature engineering expertise
Background in AI safety, alignment research, or RLHF-adjacent work
Experience mentoring or reviewing technical work produced by other ML practitioners
Why Join Us
Work directly with world-leading AI research teams and labs on genuinely frontier projects
Fully remote and asynchronous — work when and where you're most effective
Freelance autonomy with meaningful, intellectually stimulating task-based work
Gain rare, hands-on exposure to how cutting-edge LLMs are trained and evaluated
Contribute to AI systems that millions of people will rely on
Potential for ongoing work and contract extension as new projects launch

Work arrangement
Yes