Machine Learning Engineer
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.
About the job
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.
Skills
TensorFlow, PyTorch, Caffe, Python, C++, Scala, Spark, SQL, Cassandra, Docker
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