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Machine Learning Engineer

Posted 5 months agoInactiveViewed

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💎 Seniority level: Middle, 3-5 years

📍 Location: United States, Sweden, Pacific Time, Central European Time

🔍 Industry: AI, Machine Learning

🗣️ Languages: English

⏳ Experience: 3-5 years

🪄 Skills: PythonSQLMachine LearningPyTorchRubyRuby on RailsSnowflakeSoftware Engineering

Requirements:
  • Demonstrated proficiency in AI/ML with a track record of at least 3-5 years experience building machine learning systems.
  • Up to speed on the latest in NLP & LLMs, and proficient in data curation, modeling, and training models.
  • Skills required for shipping and maintaining ML in production settings.
  • Bachelor's degree in Computer Science, Engineering, AI, Mathematics, or related field; Master's degree or PhD a plus.
  • A solid engineering background with a robust foundation in software engineering principles.
  • Proficient in Python and SQL; our AI stack uses Python & PyTorch and interfaces with Ruby on Rails (bonus if you know it, but not required).
  • We write a lot of SQL queries on top of Snowflake to pull data.
Responsibilities:
  • As an ML engineer on Supernormal's AI team, you will be responsible for the end-to-end development of our AI solutions for meeting notes, question answering, and task completion.
  • Your work will encompass LLM API calls, custom model training and deployment, speech recognition, quality evaluation and fixes, retrieval augmented generation, and much more.
  • You'll play a key role in optimizing for cost, latency, and quality.
  • Some of the projects you'll work on include prompt engineering using state-of-the-art techniques to improve the core meeting assistant scenarios.
  • Building and shipping custom machine learning models to augment the AI stack, including improving transcript quality and reducing tokens sent to APIs.
  • Training and deploying custom large language models from open source using state-of-the-art techniques and fine-tuning foundation models for various business purposes.
  • Developing new product experiences using NLP & LLMs that get better based on user feedback & iteration.
  • Defining and improving business & product metrics to optimize the quality and cost of AI usage.
  • Improving LLM-powered search and question answering over sets of meetings.
  • Advocating for, and building, new and better ways of doing things.
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