Machine Learning Engineer

Posted 2024-09-11

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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 RailsSnowflake

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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Machine Learning Engineer

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