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Wordbricks
Posted 29 days agoVerified live 2d ago

Machine Learning Engineer, Data & Training

Brief overview

Remote
3+ yrsMinimum
Machine Learning EngineeringData EngineeringPythonData Collection and Labeling PipelinesDataset CurationModel Training and Fine-TuningModel EvaluationReinforcement Learning from Human Feedback (RLHF)Preference DataSynthetic Data GenerationLLM Evaluation DesignData Quality

About the company

Wordbricks logo
Wordbrickswordbricks.ai

Wordbricks specializes in empowering diverse communities with Generative AI tools to solve problems to increase the productivity.

Job description

Summary

Wordbricks is an AI company focused on making complex technology accessible and improving how people work with messy data. The Machine Learning Engineer, Data & Training will own the workflow from raw data through trained models, including data collection, dataset curation, model training, fine-tuning, and evaluation, while partnering with product and engineering teams to deliver user-facing improvements.

Responsibilities

  • Build and run data collection and labeling pipelines
  • Own dataset curation, quality, and versioning
  • Train, fine-tune, and evaluate the models behind our products
  • Work with product and engineering to turn model improvements into user-visible wins

Skills

  • 3+ years of experience in ML engineering, data engineering, or a related role
  • Hands-on experience training or fine-tuning models (LLMs a plus)
  • Strong Python and data tooling skills
  • You care deeply about data quality and can prove it
  • Experience with RLHF, preference data, or synthetic data generation
  • Background in evaluation design for LLM systems
  • Contributions to open-source models or datasets

Qualifications

Must Haves

  • 3+ years of experience in ML engineering, data engineering, or a related role
  • Hands-on experience training or fine-tuning models (LLMs a plus)
  • Strong Python and data tooling skills
  • You care deeply about data quality and can prove it

Nice to Haves

  • Experience with RLHF, preference data, or synthetic data generation
  • Background in evaluation design for LLM systems
  • Contributions to open-source models or datasets

Benefits

  • 100% Remote Work
  • Flexible Work Schedule
  • Annual Company Retreat
  • Comprehensive Insurance Coverage and Retirement Benefits

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