Machine Learning Researcher
Freshdesk is looking for Machine Learning Researchers [Multiple Openings] to help us scale our intelligent social media monitoring product. Machine Learning team @ Social is composed of full stack engineers & researchers who are responsible for end-to-end ML infrastructure. The ideal candidate should have a proven track record of building ml models, excellent understanding of mathematics and state-of-the-art deep learning techniques.
At Freshdesk, we believe in strong team, solid vision and consistent execution. One dimension which binds these characteristics is to have a strong product culture. A strong product culture means that the team understands the importance of continuous execution and learning. We:
• Understand the need to break things in order to learn, but we break them faster and mitigate the risks.
• Respect and value our designers and engineers.• Understand the power of motivated product team and engineering excellence.
• Understand the importance of “fail early fail fast”.
• Understand the need for continuous innovation.
• Know that great products are the result of team collaboration.
4+ years relevant experience in the industry/academia in machine learning with:
1. Very Strong Understanding of ML concepts - Probabilistic Models, Supervised and Unsupervised Learning, Neural Networks, Support Vector Machines, Dimansionality Reduction.
2. Solid understanding of mathematical underpinnings behind Machine Learning algorithms such as Probability theory, Statistics, Linear Algebra, Optimization Techniques.
3. Exposure to current state-of-the-art DeepLearning techniques - Recurrent Neural Nets, Convonutional Neural Nets, Embeddings, Reinforcement Learning.
4. Excellent understanding of the fundamentals behind working of Neural Nets.
5. Exposure to libraries such as theano, tensorflow, keras, torch, caffe.
6. Strong working knowledge in Python.
7. Solid engineering and coding skills. Ability to write high performance code.
8. Participated in Kaggle competitions.
9. Read, understand and implement research papers.
• Expirence with Keras/Theano/TensorFlow.
• Updated with the latest fronteriers of research in Machine Learning and Deep Learning
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