Senior Machine Learning Engineer

Alignerr Corp.; Mumbai; À distance; INR 7 857 000 - 15 714 000; Temps partiel

Résumé du poste

Alignerr is seeking a Senior Machine Learning Engineer (AI Training) to craft structured reasoning traces that guide how state-of-the-art AI systems think, plan, and solve problems. This fully remote, hourly contract role offers flexible scheduling for experienced ML professionals.

You will author high-fidelity traces, design multi-step reasoning strategies, and refine traces to reflect sound ML thinking and reliable model behavior on cutting-edge language models.

Qualifications

• Extensive experience in training large language models.
• Ability to design structured reasoning traces.
• Strong understanding of model behaviour and evaluation.

Responsabilités

• Author complex reasoning traces for LLM planning and tool use.
• Design traces capturing multi-step problem decomposition.
• Review traces to ensure sound ML thinking and generalizable behavior.

Connaissances

ML reasoning traces

LLM training

Problem decomposition

Model evaluation

Outils

Deep learning frameworks

Experiment tracking

Description du poste

Senior Machine Learning Engineer (AI Training)

About The Role

What if your deep knowledge of machine learning could directly shape how the world's most advanced AI systems think, plan, and solve problems? We're looking for Senior Machine Learning Engineers to help train cutting-edge large language models - not by writing traditional code, but by crafting the structured reasoning traces that teach AI how to think step by step. Your architectural intuition and understanding of model behaviour will have a direct, lasting impact on how next-generation AI approaches real-world challenges.

This is a fully remote, flexible contract role built for experienced ML professionals who want to work at the frontier of AI development - on their own schedule.

• 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 an LLM should plan, use tools, and make decisions across sophisticated technical tasks
• Design structured traces that capture multi-step problem decomposition, logical inference, and real-world decision-making strategies
• Review and refine reasoning traces to ensure they reflect sound ML thinking and produce reliable, generalizable model behaviour