Develops, trains, deploys, and optimizes deep learning models for production use cases involving large datasets. Requires 1-2 years ML experience, Python proficiency, and familiarity with ML frameworks like TensorFlow or PyTorch.
Salary not listed
On-site1+ YOEML Engineering
About the role
Responsibilities
Design and code neural networks, gather and refine data, train and tune models, deploy at scale with high throughput and uptime, analyze results to improve accuracy and speed.
Interface with Backend and DevOps teams and internal data labeling services.
Utilize OWASP top 10 techniques to secure code from vulnerabilities.
Maintain awareness of industry best practices for data maintenance handling.
Adhere to policies for protection of information assets and report violations.
Requirements
Undergraduate or graduate degree in computer science or similar technical field, with significant coursework in mathematics or statistics.
1-2 years industry machine learning experience.
Successfully trained and deployed a deep learning model (image, NLP, video, or audio) into production with improved performance.
Strong experience with high-level ML frameworks such as TensorFlow, Caffe, or Torch, and familiarity with others.
Strong Python experience, especially with ML frameworks.
Capable of prototyping data pipelines with Python, Node, bash, Linux CLI for large datasets.
Working knowledge of C++, Scala/Spark, SQL, Cassandra, Docker.
Up-to-date on latest deep neural net research and architectures.
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