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:

• Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers.

• Take models currently in production, identify areas for improvement, improve them using retraining and hyperparameter searches, then deploy without regressing on core model characteristics

• Work with product and research teams to identify opportunities for improvement in our current product line and for enabling upcoming product lines

• Work with massive datasets to develop both generic models as well as fine tune models for specific products

• 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

• 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:

• Extensive experience using computer vision, deep learning and deep reinforcement Learning, or natural language processing in a production environment

• Solid background in algorithms, data structures, and object-oriented programming

• Strong programing skills in Python or Javascript, experience in Tensorflow or PyTorch

Nice to Haves:

• Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization

• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment

• Experience with generative AI models