Alignerr
Senior Machine Learning Engineer
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Senior Machine Learning Engineer (AI Training)
About The Role
What if your deep expertise in machine learning could directly shape how the next generation of AI systems reason, plan, and solve complex problems?
We're looking for Senior Machine Learning Engineers to work on one of the most technically challenging and impactful problems in AI today: teaching large language models how to think. You'll author high-fidelity reasoning traces — structured, step-by-step records of how an LLM should plan, use tools, and arrive at decisions — creating the training data that makes AI more reliable and capable in real-world scenarios.
This is a fully remote, flexible contract role built for senior-level engineers who want to work at the frontier of AI development without the constraints of a full-time position.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
- Author complex, high-fidelity reasoning traces that document how LLMs should approach sophisticated technical tasks — including planning, tool use, and multi-step decision making
- Design and implement data strategies that help AI models navigate intricate, real-world scenarios with greater reliability
- Review and evaluate reasoning traces for logical consistency, structural quality, and completeness
- Decompose advanced ML problems into clear, well-documented reasoning steps that serve as model training examples
- Apply your knowledge of LLM architectures and training methodologies to ensure traces meet rigorous quality standards
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Who You Are
- Experienced machine learning engineer or researcher with deep knowledge of model reasoning, architecture, and training
- Skilled at breaking down complex technical problems into logical, clearly documented steps
- Familiar with advanced LLM evaluation techniques and how model behavior is shaped by training data
- Detail-oriented and systematic — you hold yourself to high standards of precision and clarity
- Self-directed and comfortable working asynchronously in a remote environment
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Nice to Have
- Prior experience with data annotation, data quality assurance, or AI evaluation systems
- Top-tier Kaggle competition results (Grandmaster or Master level) demonstrating advanced model performance and feature engineering expertise
- Hands-on experience with RLHF, chain-of-thought prompting, or other reasoning-focused training methodologies
- Background in AI safety, interpretability, or alignment research
Why Join Us
- Work directly with world-leading AI research teams on problems that matter
- Fully remote and flexible — work when and where it suits you, on your own schedule
- Freelance autonomy with the substance of meaningful, high-impact technical work
- Gain rare, hands-on exposure to how frontier LLMs are trained and evaluated
- Contribute to advancing AI systems that are more reliable, transparent, and capable in the real world
- Potential for ongoing work and contract extension as new projects launch
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