The goal of a Machine Learning Engineer at Scale is to bring techniques in the fields of computer vision, deep learning and deep reinforcement learning, or natural language processing into a production environment to improve Scale's products and customer experience. Our research engineers take advantage of our unique access to massive datasets to deliver improvements to our customers.
We are building a large hybrid human-machine system in service of ML pipelines for Federal Government customers. We currently complete millions of tasks a month, and will grow to complete billions of tasks monthly.
You will:
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Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers.
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Take models currently in production, identify areas for improvement, improve them using retraining and hyperparameter searches, then deploy without regressing on core model characteristics
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Work with product and research teams to identify opportunities for improvement in our current product line and for enabling upcoming product lines
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Work with massive datasets to develop both generic models as well as fine tune models for specific products
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Build the scalable ML platform to automate our ML service
- Be a representative for how to apply machine learning and related techniques throughout the engineering and product organization
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Be able, and willing, to multi-task and learn new technologies quickly
- This role will require an active security clearance or the ability to obtain a security clearance.
Ideally You’d Have:
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Extensive experience using computer vision, deep learning and deep reinforcement Learning, or natural language processing in a production environment
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Solid background in algorithms, data structures, and object-oriented programming
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Strong programing skills in Python or Javascript, experience in Tensorflow or PyTorch
Nice to Haves:
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Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
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Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
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Experience with generative AI models
