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