Senior Machine Learning Engineer
About this role
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
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