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Sr. Machine Learning Engineer

🕒 15 days ago
Machine LearningNLPTransformersEmbeddings

📜 Description

  • Own machine learning problems end to end — from data exploration and modeling through deployment, monitoring, and iteration in production.
  • Build NLP, LLM, and agentic systems at enterprise scale, including retrieval-based architectures and multi-agent workflows.
  • Develop ranking, recommendation, prediction, and optimization models that are explainable rather than black-box.
  • Partner with Product and Go-to-Market to turn ambiguous business problems into shipped capabilities.
  • Improve the performance, scalability, and reliability of production ML systems, and help shape AI platform architecture.
  • Explain your work clearly to technical and non-technical audiences, and engage with customers when needed.

🛠️ Requirements

  • 8+ years of industry experience building and deploying machine learning systems in production, with clear end-to-end ownership.
  • Strong foundation in machine learning and applied statistics, with hands-on depth in NLP, transformers, embeddings, and retrieval-based systems.
  • Practical experience with modern GenAI tooling such as LangGraph, LangChain, or Amazon Bedrock.
  • Strong Python skills and experience building distributed ML pipelines on cloud infrastructure (AWS, Databricks, or equivalent).
  • Solid grasp of feature engineering, model evaluation, and MLOps practices.
  • A product mindset — you want to build AI products customers use, and you measure yourself on customer impact.
  • Excellent communication: you can explain complex technical work clearly, tell the story of what you've built and why, and hold your own with product and business partners.
  • Comfort with ambiguity and the judgment to drive execution independently.

Benefits

  • Paid parental leave
  • Generous paid time-off and holidays
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