Senior Machine Learning Engineer (AI Data Trainer)
About The Role
What if your deep knowledge of machine learning could directly shape how the world's most advanced AI systems reason, plan, and make decisions We're looking for Senior Machine Learning Engineers to author high-fidelity reasoning traces — the structured, step-by-step records of how an AI thinks through complex real-world problems.
This is frontier work. The data you create will be used to train next-generation LLMs to reason more reliably, use tools more effectively, and make better decisions at scale. If you've spent years thinking deeply about model behavior, problem decomposition, and intelligent system design — this is where that expertise makes a direct, lasting impact.
This is a fully remote, flexible contract role. You set your schedule and work at your own pace.
- 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 across sophisticated technical tasks
- Design structured step-by-step decision paths that reflect expert-level thinking — including tool use, branching logic, and error recovery
- Review and mentor other contributors traces to ensure quality, consistency, and alignment with training goals
- Develop data strategies that help LLMs navigate intricate, real-world scenarios with greater reliability
- Apply senior-level ML insights to ensure traces accurately reflect how strong reasoning systems operate
Who You Are
- Experienced in machine learning, AI systems, or a closely related technical field — with a genuine focus on model reasoning and decision-making
- Skilled at decomposing complex, ambiguous problems into clear, logical, well-documented steps
- Deeply familiar with LLM evaluation and training methodologies — you understand what good reasoning looks like and why it matters
- A precise, structured thinker who can translate expert intuition into clear, reproducible logic
- Self-directed and reliable — you produce high-quality work without hand-holding
Nice to Have
- Prior experience with data annotation, data quality review, or AI evaluation systems
- Top-tier Kaggle competition results (Grandmaster or Master level) — demonstrating elite-level understanding of model performance and feature engineering
- Background in agentic AI systems, tool-use frameworks, or chain-of-thought research
- Experience contributing to or evaluating RLHF, RLAIF, or similar alignment training pipelines
Why Join Us
- Work directly with leading AI research labs on problems that sit at the frontier of machine intelligence
- Fully remote and asynchronous — work when, where, and how it suits you
- Freelance autonomy paired with meaningful, intellectually stimulating work
- Your contributions have a direct, traceable impact on how the next generation of AI reasons and acts
- Potential for ongoing work and contract extension as new projects launch
